Method and system for video processing to remove noise from a digital video sequence containing a modulated light signal

ABSTRACT

In one aspect, the present disclosure relates to a method for removing noise from a digital video sequence containing a modulated light signal emitted from a beacon light source. In some embodiments, the method includes electronically receiving, by an image sensor of a device, a digital video sequence of a scene, calculating noise from the digital video sequence, wherein the noise comprises information within the digital video sequence corresponding to the un-modulated illumination of the scene, reducing the noise from the digital video sequence to obtain an isolated digital video sequence of the modulated illumination of the scene, and demodulating the emitted light signal from the isolated digital video sequence.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims benefit under 35 U.S.C.§120 to U.S. Utility application Ser. No. 13/718,233, entitled “Methodand System for Configuring an Imaging Device for the Reception ofDigital Pulse Recognition Information”, filed Dec. 18, 2012.

U.S. Utility application Ser. No. 13/718,233, entitled “Method andSystem for Configuring an Imaging Device for the Reception of DigitalPulse Recognition Information”, filed Dec. 18, 2012, is a continuationof and claims benefit under 35 U.S.C. §120 to U.S. Utility applicationSer. No. 13/526,656, entitled “Method and System for Configuring anImaging Device for the Reception of Digital Pulse RecognitionInformation”, filed Jun. 19, 2012, now U.S. Pat. No. 8,334,898.

U.S. Utility application Ser. No. 13/526,656, entitled “Method andSystem for Configuring an Imaging Device for the Reception of DigitalPulse Recognition Information”, filed Jun. 19, 2012, now U.S. Pat. No.8,334,898, claims priority under 35 U.S.C. §119(e) to U.S. ProvisionalApplication No. 61/639,428, filed Apr. 27, 2012 and entitled “Method ForMeasuring Modulation Frequency Of A Light Source,” the entire contentsof which are incorporated herein by reference.

U.S. Utility application Ser. No. 13/526,656, entitled “Method andSystem for Configuring an Imaging Device for the Reception of DigitalPulse Recognition Information”, filed Jun. 19, 2012, now U.S. Pat. No.8,334,898, claims priority under 35 U.S.C. §119(e) to U.S. ProvisionalApplication No. 61/635,413, filed Apr. 19, 2012 and entitled “DigitalPulse Recognition Demodulation Techniques For Light Based Positioning,”the entire contents of which are incorporated herein by reference.

U.S. Utility application Ser. No. 13/526,656, entitled “Method andSystem for Configuring an Imaging Device for the Reception of DigitalPulse Recognition Information”, filed Jun. 19, 2012, now U.S. Pat. No.8,334,898, claims priority under 35 U.S.C. §119(e) to U.S. ProvisionalApplication No. 61/567,484, filed Dec. 6, 2011 and entitled “Systems AndMethods For Light Based Location,” the entire contents of which areincorporated herein by reference.

U.S. Utility application Ser. No. 13/526,656, entitled “Method andSystem for Configuring an Imaging Device for the Reception of DigitalPulse Recognition Information”, filed Jun. 19, 2012, now U.S. Pat. No.8,334,898, claims priority under 35 U.S.C. §119(e) to U.S. ProvisionalApplication No. 61/511,589, filed Jul. 26, 2011 and entitled “SystemUsing Optical Energy For Wireless Data Transfer,” the entire contents ofwhich are incorporated herein by reference.

U.S. Utility application Ser. No. 13/526,656, entitled “Method andSystem for Configuring an Imaging Device for the Reception of DigitalPulse Recognition Information”, filed Jun. 19, 2012, now U.S. Pat. No.8,334,898, is a continuation-in-part of and claims benefit under 35U.S.C. §120 to U.S. Utility application Ser. No. 13/446,520, entitled“Method And System For Tracking And Analyzing Data Obtained Using ALight Based Positioning System,” filed Apr. 13, 2012, which is acontinuation of and claims benefit under 35 U.S.C. §120 to U.S. Utilityapplication Ser. No. 13/445,019, entitled “Single Wavelength LightSource for Use in Light Based Positioning System,” filed Apr. 12, 2012;U.S. Utility application Ser. No. 13/435,448, entitled “A Method andSystem for Calibrating a Light Based Positioning System,” filed Mar. 30,2012; U.S. Utility application Ser. No. 13/422,591, entitled “SelfIdentifying Modulated Light Source,” filed Mar. 16, 2012; U.S. Utilityapplication Ser. No. 13/422,580, entitled “Light Positioning SystemUsing Digital Pulse Recognition,” filed Mar. 16, 2012, now U.S. Pat. No.8,248,467; U.S. Utility application Ser. No. 13/369,147, entitled“Content Delivery Based on a Light Positioning System,” filed Feb. 8,2012; and U.S. Utility application Ser. No. 13/369,144, entitled“Independent Beacon Based Light Positioning System,” filed Feb. 8, 2012.

U.S. Utility application Ser. No. 13/526,656, entitled “Method andSystem for Configuring an Imaging Device for the Reception of DigitalPulse Recognition Information”, filed Jun. 19, 2012, now U.S. Pat. No.8,334,898, is also a continuation-in-part of and claims benefit under 35U.S.C. §120 to U.S. Utility application Ser. No. 13/446,506, entitled“Method And System For Determining the Position Of A Device In A LightBased Positioning System Using Locally Stored Maps,” filed Apr. 13,2012, which is a continuation of and claims benefit under U.S.C. §120 toU.S. Utility application Ser. No. 13/445,019, entitled “SingleWavelength Light Source for Use in Light Based Positioning System,”filed Apr. 12, 2012; U.S. Utility application Ser. No. 13/435,448,entitled “A Method and System for Calibrating a Light Based PositioningSystem,” filed Mar. 30, 2012; U.S. Utility application Ser. No.13/422,591, entitled “Self Identifying Modulated Light Source,” filedMar. 16, 2012; U.S. Utility application Ser. No. 13/422,580, entitled“Light Positioning System Using Digital Pulse Recognition,” filed Mar.16, 2012, now U.S. Pat. No. 8,248,467; U.S. Utility application Ser. No.13/369,147, entitled “Content Delivery Based on a Light PositioningSystem,” filed Feb. 8, 2012; and U.S. Utility application Ser. No.13/369,144, entitled “Independent Beacon Based Light PositioningSystem,” filed Feb. 8, 2012.

U.S. Utility application Ser. No. 13/526,656, entitled “Method andSystem for Configuring an Imaging Device for the Reception of DigitalPulse Recognition Information”, filed Jun. 19, 2012, now U.S. Pat. No.8,334,898, is also related to the following applications, filedconcurrently with U.S. Utility application Ser. No. 13/526,656, theentire contents of which are incorporated herein by reference: U.S.patent application Ser. No. 13/526,808, filed on Jun. 19, 2012, entitled“Method and System for Modulating a Light Source in a Light BasedPositioning System using a DC Bias”, now U.S. Pat. No. 8,334,901; U.S.patent application Ser. No. 13/526,788, filed on Jun. 19, 2012, entitled“Method And System For Modulating A Light Source In A Light BasedPositioning System Using A DC Bias”; U.S. patent application Ser. No.13/526,812, filed on Jun. 19, 2012, entitled “Device For Dimming ABeacon Light Source Used In A Light Based Positioning System”; U.S.patent application Ser. No. 13/526,781, filed on Jun. 19, 2012, entitled“Method and System for Modifying a Beacon Light Source for Use in aLight Based Positioning System”; U.S. patent application Ser. No.13/526,773, filed on Jun. 19, 2012, entitled “Method And System ForDigital Pulse Recognition Demodulation”, now U.S. Pat. No. 8,416,290;U.S. patent application Ser. No. 13/526,814, filed on Jun. 19, 2012,entitled “Method And System For Video Processing To Determine DigitalPulse Recognition Tones”, now U.S. Pat. No. 8,520,065; and U.S. patentapplication Ser. No. 13/526,779, filed on Jun. 19, 2012, entitled“Method And System For Demodulating A Digital Pulse Recognition SignalIn A Light Based Positioning System Using A Fourier Transform”, now U.S.Pat. No. 8,436,896.

The above referenced applications are hereby incorporated by referencein their entirety.

FIELD OF THE DISCLOSURE

This disclosure relates generally to a system and method for removingnoise from a digital video sequence containing a modulated light signalemitted from a beacon light source.

BACKGROUND

Indoor positioning services refers to methods where networks of devicesand algorithms are used to locate mobile devices within buildings.Indoor positioning is regarded as a key component of location-awaremobile computing and is a critical element in providing augmentedreality (AR) services, Location-aware computing refers to applicationsthat utilize a user's location to provide content relevant to thatlocation. Additionally, AR is a technology that overlays a virtual spaceonto a real (physical) space. To successfully enable AR andlocation-aware computing, accurate indoor positioning is a keyrequirement.

Global Positioning Systems (GPS) loses significant power when passingthrough construction materials, and suffers from multi-path propagationeffects that make it unsuitable for indoor environments. Techniquesbased on received signal strength indication (RSSI) from WiFi andBluetooth wireless access points have also been explored. However,complex indoor environments cause radio waves to propagate in dynamicand unpredictable ways, limiting the accuracy of positioning systemsbased on RSSI. Ultrasonic techniques (US), which transmit acoustic wavesto microphones, are another method which can be used to approximateindoor position. They operate at lower frequencies than systems based onWiFi and attenuate significantly when passing through walls. Thispotentially makes US techniques more accurate than WiFi or Bluetoothtechniques.

Optical indoor positioning techniques use optical signals, eithervisible or infrared, and can be used to accurately locate mobile devicesindoors. These are more accurate than the approaches mentionedpreviously, since optical signals are highly directional and cannotpenetrate solid objects. However this directionality limits thepotential reliability of optical signals, since difficulty in aligningthe receiver and transmitter can occur.

SUMMARY

In one aspect, the present disclosure relates to a system and method forremoving noise from a digital video sequence containing a modulatedlight signal emitted from a beacon light source. In some embodiments,the method includes initializing one or more imaging sensors of theimaging device, determining a subset of the one or more imaging sensorsto configure, setting a configuration for each of the one or moreimaging sensors of the subset by defining a region of interest as ametering area for each of the one or more imaging sensors of the subsetand automatically adjusting a setting for each of the one or moreimaging sensors of the subset, and adjusting input parameters of ademodulation function based on a device profile of the imaging device.In some embodiments, the adjusted setting is locked to prevent furtheradjustment of the adjusted setting. In some embodiments, the deviceprofile is calculated by the imaging device. In some embodiments, thedevice profile is retrieved from a remote server. In some embodiments,the device profile includes at least rolling shutter speed of the one ormore imaging sensors of the imaging device. In some embodiments, thedemodulation function calculates a frequency content of an imagecaptured by the one or more imaging sensors based on a detected stripewidth. In some embodiments, a frequency corresponding to each stripewidth is adjusted based on the rolling shutter speed of the one moreimaging sensors from the device profile. In some embodiments, adjustinga setting for the imaging sensor includes adjusting one of exposure,focus, saturation, white balance, zoom, contrast, brightness, gain,sharpness, ISO, resolution, image quality, scene selection, and meteringmode. In some embodiments, the defined region of interest is thebrightest area of an image captured by a particular imaging sensor andautomatically adjusting a setting for the particular imaging sensorincludes lowering the exposure time for the particular imaging sensor.In some embodiments, the one or more imaging sensors of the imagingdevice includes at least a front-facing imaging sensor and a rear-facingimaging sensor. In some embodiments, initializing one or more imagingsensors of the imaging device includes setting at least one of framerate, data format, encoding scheme, and color space for one of the oneor more imaging sensors. In some embodiments, determining a subset ofthe one or more imaging sensors to configure is based on at least one ofdesired level of power consumption, desired level of demodulationaccuracy, amount of time since each sensor was last modified,environmental conditions, and the battery state of the imaging device.In some embodiments, the subset of the one or more imaging sensors toconfigure is fewer than all of the one or more imaging sensors of theimaging device. In some embodiments, the subset of the one or moreimaging sensors to configure is all of the one or more imaging sensorsof the imaging device.

Another aspect of the present disclosure related to an image detectionapparatus for capturing digital images for digital pulse recognitiondemodulation. In some embodiments, the image detection apparatusincludes one or more imaging sensors and a processor in communicationwith the one or more imaging sensors configured to initialize one ormore imaging sensors, determine a subset of the one or more imagingsensors to configure, set a configuration for each of the one or moreimaging sensors of the subset by defining a region of interest as ametering area each of the one or more imaging sensors of the subset andautomatically adjusting a setting for each of the one or more imagingsensors of the subset, and adjust input parameters of a demodulationfunction based on a device profile of the image detection apparatus. Insome embodiments, the processor is configured to lock the adjustedsetting to prevent further adjustment of the adjusted setting. In someembodiments, the processor is configured to calculate the deviceprofile. In some embodiments, the processor is configured to retrievethe device profile from a remote server. In some embodiments, the deviceprofile includes at least rolling shutter speed of the one or moreimaging sensors of the image detection apparatus. In some embodiments,the processor is configured to calculate frequencies using thedemodulation function based on a detected stripe width in imagescaptured by the one or more imaging sensors. In some embodiments, afrequency corresponding to each stripe width is adjusted based on therolling shutter speed of the one more imaging sensors from the deviceprofile. In some embodiments, adjusting a setting for the imaging sensorincludes adjusting one of exposure, focus, saturation, white balance,zoom, contrast, brightness, gain, sharpness, ISO, resolution, imagequality, scene selection, and metering mode. In some embodiments, thedefined region of interest is the brightest area of an image captured bya particular imaging sensor and automatically adjusting a setting forthe particular imaging sensor includes lowering the exposure time forthe particular imaging sensor. In some embodiments, the one or moreimaging sensors includes at least a front-facing imaging sensor and arear-facing imaging sensor. In some embodiments, the processor isconfigured to initialize one or more imaging sensors by setting at leastone of frame rate, data format, encoding scheme, and color space for oneof the one or more imaging sensors. In some embodiments, the processoris configured to determine a subset of the one or more imaging sensorsto configure based on at least one of desired level of powerconsumption, desired level of demodulation accuracy, amount of timesince each sensor was last modified, environmental conditions, and thebattery state of the image detection apparatus. In some embodiments, thesubset of the one or more imaging sensors to configure is fewer than allof the one or more imaging sensors of the image detection apparatus. Insome embodiments, the subset of the one or more imaging sensors toconfigure is all of the one or more imaging sensors of the imagedetection apparatus.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a representation of a mobile device receiving light from a LEDlight source.

FIG. 2 is a representation of a mobile device receiving multiple sourcesof light simultaneously from multiple LED light sources.

FIG. 3 is a representation of the internal components commonly found ina LED light source that is capable of being modulated to send digitaldata.

FIG. 4 illustrates information which can be optically transmitted froman LED light source.

FIG. 5 is a representation of the components which are commonly found inmobile devices which enable them to receive optical signals from LEDsources.

FIG. 6 is a representation of multiple LED light sources sending uniqueinformation to multiple mobile devices.

FIG. 7 illustrates the process of a mobile device sending identificationinformation and receiving location information via a network to aserver.

FIG. 8 illustrates the high level contents of the server which includesdatabases and web services for individual areas enabled with lightpositioning systems.

FIG. 9 illustrates the components inside the databases.

FIG. 10 illustrates the information contained in the Light IDs database.

FIG. 11 illustrates the information contained in the Maps database.

FIG. 12 illustrates the information contained in the Content database.

FIG. 13 illustrates the information contained in the Analytics database.

FIG. 14 illustrates the process of a mobile device receiving locationand content information via a light-based positioning system.

FIG. 15 is a process illustrating the background services and how theyactivate various sensors contained inside the mobile device.

FIG. 16 illustrates the process of combining multiple informationsources with a light-based positioning service.

FIG. 17 illustrates how a client accesses multiple light positioningenabled locations with multiple mobile devices.

FIGS. 18A-C are representations of a light source undergoingpulse-width-modulation at varying duty cycles, according to someembodiments of the present disclosure.

FIGS. 19A-C are representations of a light source undergoingpulse-width-modulation at varying duty cycles with a DC offset,according to some embodiments of the present disclosure.

FIG. 20 is a block diagram of a DPR modulator with a dimming controlsystem for a light source, according to some embodiments of the presentdisclosure.

FIG. 21 is a representation of a block diagram of a DPR modulator,according to some embodiments of the present disclosure.

FIG. 22 is a block diagram of an encoder for DPR modulation, accordingto some embodiments of the present disclosure.

FIG. 23 is a block diagram for a waveform generator for DPR modulation,according to some embodiments of the present disclosure.

FIG. 24 is a block diagram of a symbol selector system module, which isused to select an appropriate symbol for use in DPR modulation,according to some embodiments of the present disclosure.

FIG. 25 is a plot of a camera sampling function, according to someembodiments of the present disclosure.

FIG. 26 is a plot of a modulated illumination function undergoing DPRmodulation at a frequency of 300 Hz, according to some embodiments ofthe present disclosure.

FIG. 27 is a plot of a convolution of a camera sampling function and aDPR modulated light signal, according to some embodiments of the presentdisclosure.

FIG. 28 is a model of the CMOS sampling function for a rolling shutter,according to some embodiments of the present disclosure.

FIG. 29 is a plot of a sampling function for a CMOS rolling shutter overmultiple frames, according to some embodiments of the presentdisclosure.

FIG. 30 is a high level flow chart of an algorithm for configuring amobile device to receive DPR modulated signals, according to someembodiments of the present disclosure.

FIG. 31 is a high level flow chart of an algorithm for minimizing andlocking camera settings using existing mobile device applicationprogramming interfaces (APIs), according to some embodiments of thepresent disclosure.

FIG. 32 is a high level flow chart of an algorithm for receiving DPRsignals on an image sensor, according to some embodiments of the presentdisclosure.

FIG. 33 is a high level flow chart of an algorithm for determining tonesembedded within a DPR illuminated area, according to some embodiments ofthe present disclosure.

FIG. 34 is a high level flow chart of an algorithm for performingbackground subtraction on images gathered from a DPR illuminated scene,according to some embodiments of the present disclosure.

FIG. 35 is a high level flow chart of an algorithm for performing motioncompensation on video frames when performing DPR demodulation, accordingto some embodiments of the present disclosure.

FIG. 36 is a photograph of a surface under illumination from DPRmodulated signals, according to some embodiments of the presentdisclosure.

FIG. 37 is a post-processed image of a DPR modulated scene afterperforming background subtraction, according to some embodiments of thepresent disclosure.

FIG. 38 is a post-processed image of a DPR modulated scene after rowaveraging, according to some embodiments of the present disclosure.

FIG. 39 is a plot of the 1-D spectral content of a DPR modulatedsurface, according to some embodiments of the present disclosure.

FIG. 40 is a plot of the 1-D spectral content of a DPR modulated surfaceafter removing DC bias, according to some embodiments of the presentdisclosure.

FIG. 41 is a 2-D FFT of a DPR modulated surface, according to someembodiments of the present disclosure.

FIG. 42 is a 2-D FFT of a DPR modulated surface after applying a lowpass filter, according to some embodiments of the present disclosure.

FIG. 43 is a 2-D FFT of a DPR modulated surface after applying a highpass filter, according to some embodiments of the present disclosure.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Systems and methods are provided that disclose providing a positioningservice for devices based on light received from one or more lightsources. This light-based positioning service uses light informationtransmitted by each light source to determine the position of thedevice. The device captures the one or more light sources and is thenable to detect the information transmitted by each of the light sources.The light information can include an identification code that is used toidentify the position of the light source. By capturing more than onelight source on the device the accuracy of the device's position can beimproved. The position information can then be used to provide relevantcontent information to the user. The light sources are each independentbeacons that transmit individual identification information throughlight.

In some embodiments light sources are used to provide an indoorpositioning service to mobile devices. Each light source is given anidentification code, corresponding to an associated database, whichcontains information that ties the light source to specific locationdata. The identification codes are broadcasted through visible light bymodulating the LED light source. The modulation occurs at speeds thatare undetectable by the human eye, yet appropriate to be received by acamera equipped mobile device. The mobile device receives theidentification information, and uses it to lookup its indoor position inthe form of location data. Since the identification information istransmitted through visible light, which is highly directional, themobile device is known to be within the line of sight of the LED lightsource. Since the indoor position of the LED light source is known frombuilding floor plans and lighting plans, the corresponding indoorposition of the mobile device can be determined.

Another embodiment describes a scenario where a mobile device is in viewof three or more LED light sources. Each source emits uniqueidentification information and, with knowledge of the relative positionsof each LED light source, one can calculate the device's relativeposition in three dimensions. This process utilizes photogrammetricimage processing techniques to identify and calculate coordinates forthe positions of the light sources in order to relatively locate themobile device.

Yet another embodiment describes a system by which a mobile device 103can receive content based upon identification information received fromeither one or more LED light sources. The identification information isused to access a database that correlates LED lights and content. Anexample of such a use case would be a mobile device user in a museum,who receives identification information from a light source illuminatingan exhibit, and then uses the received identification information toobtain additional content about the exhibit.

FIG. 1 represents a mobile device 103 receiving light 102 from a LEDlight source 101. The LED light source 101 can be any lighting sourceused for general purpose, spot illumination, or backlighting. The LEDlight source can come in several form factors but is not limited to:Edison screw in, tube style, large and small object backlighting, oraccent lighting spots and strips. For the purposes of this disclosure,we consider any form of LED light as a potential source capable oftransmitting information.

Light 102 is a modulated LED light source 101, and is part of thevisible electromagnetic wireless spectrum. LEDs are considered digitaldevices which can be rapidly switched on and off, to send signals abovethe rate which the human eye can see. This allows them to be exploitedto send digital data through the visible light itself. By modulating theLEDs, turning them on and off rapidly, one can send digital informationthat is unperceivable to the human eye, but is perceivable by applicablesensors, including but not limited to image sensors and other types ofphotosensors.

There are many modulation techniques used to send information throughlight 102. One technique, “On Off Keying” (OOK), is a scheme to transmitdigital data by rapidly switching a signal source on and off. OOK is thesimplest form of amplitude-shift keying (ASK) which is a modulationtechnique that represents digital data through either the presence orabsence of a carrier wave. When communicating with visible light, thecarrier wave takes the form of the transmitted light signal. Thereforeat a rudimentary level, when the light signal is turned “on” a digital“one” is perceived, and when the light signal is turned “off” a “zero”is perceived. Furthermore the rate at which the light signal is turnedon and off represents the modulation frequency. Note that regardless ofchanging the modulation frequency, the “carrier wave” remains unchangedas this is an inherent property of the light itself. For example thecarrier wave corresponding to a blue light signal is uniquely differentthan the carrier wave corresponding to a red light signal. While thesetwo signals differ only in the wavelength specific to their perceivedcolor, they can be perceived as two discrete signals.

In addition to OOK, another possible technique is defined as “DigitalPulse Recognition” (DPR). This modulation technique exploits the rollingshutter mechanism of a complementary metal-oxide-semiconductor (CMOS)image sensor. Due to their superior energy efficiency, CMOS sensors arepreferred to charged-coupled device (CCD) sensors on mobile devices.When a CMOS image sensor with a rolling shutter takes an image, it doesnot expose the entire image simultaneously. Instead, the rolling shutterpartially exposes different portions of the frame at different points intime. Typically, this causes various unwanted effects: skew, wobble, andpartial exposure. In the presence of an LED light driven by a pulsewidth modulated signal, images received from a CMOS sensor exhibit“residual banding” in the form of visible distortions. The image appearsto have alternating dark/white stripes. The stripes are a direct resultof the rolling shutter mechanism, and their width is proportional to thefrequency of the pulse width modulated (PWM) signal. Higher frequenciescorrespond to narrower stripes, and lower frequencies result in widerstripes. Practical frequency ranges for use with this technique arebetween 60 Hz and 5000 Hz. This technique allows one to exploit therolling shutter mechanism to recover digital data from an opticallyencoded signal.

DPR has the potential for much higher data rates than both OOK andfrequency shift keying (FSK). In FSK and OOK, the camera's frame ratelimits the data rate. The highest possible data rate is half of theframe rate, since each symbol spans over two frames. In DPR modulation,a single frame is sufficient for capturing the transmitted symbol.Furthermore, symbols are not “binary”—there are can be as many as 30different possibilities for a symbol.

In the DPR modulation scheme, image processing is used to measure thestripe width of the recorded image. By successively changing the LEDdriver frequency for each frame, information is essentially transmittedthrough recognition of the band widths. In the current design, 10separate frequencies are used. For a 30 frames per second (FPS) camera,this corresponded to an effective data transfer rate of ˜100 bits persecond (bps).

Both of these techniques are interesting because they can allow thetransmission of information through single color light sources, insteadof having to create lighting sources which contain multiple colorlights. In the world of LED lighting products, white light is majorlyachieved by layering a phosphorous coating on top of blue LEDs. Thecoating creates the visible perception of “white” light, instead ofblue. The alternative to this can be achieved through combining red,green, and blue LED lights; however this approach is expensive and powerinefficient as the lumens per watt properties differ between differentcolored LEDs. Blue LEDs are generally more energy efficient than theirred and green counterparts, which is why they are used in mostcommercial LED lighting products. It is because of this reason that itmakes the most sense to use a data modulation technique that uses asingle wavelength of light, rather than multiple, because this complieswith LED lighting products.

In addition to LED light sources, other types of light sources are alsocapable of transmitting information through modulation. Alternativeincandescent and fluorescent technologies can also be exploited toachieve data transmission, however the circuitry is more complex becausethe turn on and turn off times of incandescent and fluorescent lightsare subject to additional factors.

The modulation frequency of the light source is highly dependent on thereceiving circuitry. While incandescent and fluorescent technologiesgenerally do not “flicker” on and off during the course of normaloperation, LED lighting sources are sometimes designed to flicker abovethe rate which the eye can see in order to increase their longevity, andconsume less power. Most humans cannot see flicker above 60 Hz, but inrare instances can perceive flicker at 100 Hz to 110 Hz. To combat this,lighting manufacturers design flicker above 200 Hz into their lightingproducts.

Mobile device 103 can be a smart mobile device and is most commonlyfound in the form of mobile phones, tablets, and portable laptopcomputers. In order for a mobile device 103 to receive information 102from the LED light source 101 it has an embedded or attached sensorwhich is used to receive the incoming light 102 signals. One such sensoris a camera, which has a typical frame refresh rate between fifteen andsixty frames per second (fps). The fps is directly related to the speedat which optical signals can be transmitted and received by the camera.The sensor can capture a number of successive image frames that canlater be analyzed to determine if a light source is providinginformation through light.

Mobile device 103 can include a processor, module, memory, and sensor inorder to capture and analyze light received from light sources. Themobile device can analyze the successive image frames captured by thesensor by using the module. The module can be logic implemented in anycombination of hardware and software. The logic can be stored in memoryand run by processor to modify the successive images and analyze thesuccessive images to determine information encoded in the light of oneor more light sources. The module can be built in to the mobile deviceto provide the capabilities or it can be downloaded and installed. Themodule can be an application that runs on the mobile device whenselected by a user. The module can also be used to receive content andother information related to the position of the mobile device and toprovide this content to other modules or to the mobile device.

The reception of optically transmitted information is particularlyinteresting when used as an indoor positioning system. In a light-basedpositioning system, the physical locations of light sources can be usedto approximate the relative position of a mobile device 103 within lineof sight. On the mobile side, in addition to a receiving module, themobile device 103 can use information to determine position of themobile device. The mobile device can access a data source containinginformation about where the lights are physically located to determineposition. This data source can be stored locally, or in the case wherethe mobile device 103 has a network connection, the data source could bestored on an external server 703.

For scenarios where a network connection is not available, beforeentering an indoor space the mobile device 103 could optionally downloada “map pack” containing the information used to locate itself indoors,instead of relying on an external server 703. In order to automate thisprocess, the mobile device 103 would first use an alternative existingtechnique for resolving its position and would use the gained locationinformation to download the appropriate map pack. The techniques forreceiving geo-location information include, for example, GPS, GSM, WiFi,user input, accelerometer, gyroscope, digital compass, barometer,Bluetooth, and cellular tower identification information. Thesetechniques can also be used to fill gaps between when a position of themobile device is determined using the light-based technique. Forexample, a mobile device can be placed at times so its camera does notcapture light sources. Between these times these alternative existingtechniques can be used for filling in position and location informationthat can be helpful to the user. The map pack would contain a map 902 ofthe indoor space the user is entering, locations of the lights from somesort of existing or third-party lighting plan 1103, and anylocation-dependent content 903 for the mobile device 103 to consume. Anyrequests for location information would simply access data storedlocally on the mobile device 103, and would not need to access a remoteserver via a network 601.

In terms of the experience when using a light-based positioning system,the indoor location reception and calculation can happen with little tono user input. The process operates as a background service, and readsfrom the receiving module without actually writing them to the displayscreen of the mobile device. This is analogous to the way WiFipositioning operates, signals are read in a background service withoutrequiring user interaction. The results of the received information canbe displayed in a number of ways, depending on the desired application.In the case of an indoor navigation application, the user would see anidentifying marker overlaid on a map of the indoor space they are movingaround in. In the case of content delivery, the user might see a mobilemedia, images, text, videos, or recorded audio, about the objects theyare standing in front of.

In scenarios where the mobile device 103 is in view of several lightsources, it can receive multiple signals at once. FIG. 2 is arepresentation of a mobile device 103 receiving identificationinformation 102 a-102 c from multiple LED light sources 101 a-101 c.Each light source is transmitting its own unique piece of information.In order to identify its position or receive location-based content, themobile device 103 can then use the received information to access adatabase 802 containing information about the relative positions of theLED light sources 101 a-101 c and any additional content 903. When threeor more sources of light are in view, relative indoor position can bedetermined in three dimensions. The position accuracy decreases withless than three sources of light, yet remains constant with three ormore sources. With the relative positions of lights 101 a-101 c known,the mobile device 103 can use photogrammetry to calculate its position,relative to the light sources.

Photogrammetry is a technique used to determine the geometric propertiesof objects found in photographic images. In the context of locatingmobile devices using light sources, photogrammetry refers to utilizingthe corresponding positions of LED light sources, and their positions in3-D space, to determine the relative position of a camera equippedmobile device. When three unique sources of light are seen by the cameraon a mobile device, three unique coordinates can be created from thevarious unique combinations of 101 a-101 c and their relative positionsin space can be determined.

For a mobile device 103 equipped with an image sensor we can considerthe following scenario. When multiple LED light sources appear in theimage sensors field of view, the sources appear brighter relative to theother pixels on the image. Thresholds can then be applied to the imageto isolate the light sources. For example, pixel regions above thethreshold are set to the highest possible pixel value, and the pixelregions below the threshold are set to the minimum possible pixel value.This allows for additional image processing to be performed on theisolated light sources. The end result is a binary image containingwhite continuous “blobs” where LED light sources are detected, and darkelsewhere where the sources are not detected.

A blob detection algorithm can then be used to find separate LED lightsources. A minimum of three separate LED blobs are used to resolve the3-D position of a mobile device 103. Each LED blob represents a “regionof interest” for the information reception, and is simultaneouslytransmitting a unique piece of information via the modulated visiblesignal from the light source. For the purposes of reception, each regionof interest is processed independently of other regions of interest andis considered to be uniquely identifiable. A center of mass calculationfor each region can be performed to determine the pixel coordinates ofthe center of each LED light source. This center of mass calculation isperformed for each frame to track the regions of interest as they movearound the image.

Once the regions of interest are established, a detection algorithmcaptures multiple image frames for each region of interest in order toreceive the visible light signal contained in each blob. For each framein a detected region of interest, a threshold algorithm determineswhether the frame contains a “1” (in the case of an aggregate pixelvalue above the threshold), or a “0” (in the case of an aggregate pixelvalue lower than the threshold). The threshold algorithm is used sincethe communication is asynchronous, so the camera receiver period mayoverlap between the transmission of a “1” and a “0” from the LED lightsource.

The result of converting successive image frames in a region of interestto binary values is in essence a down-sampled digital version of thesignal received from the LED light source. Next demodulation of thedown-sampled digital signal is used to recover the transmitted bits.This down sampling is used due to the fact that the signal modulationfrequency should be above the rate at which the human eye can see, andthe image sensor frame rate is typically limited to 15-30 fps.

At a lower level, the mobile device 103 processes data on aframe-by-frame basis. Each frame is split into separate regions ofinterest, based on the detection of light sources. For each region ofinterest, a thresholding algorithm is used to determine whether a givenregion is “on” or “off”. This is done by taking the average pixel valuefor the region and comparing it to the threshold value. If the region is“on”, the demodulator assumes the light source has just transmitted a“1”. If the region is “off”, the demodulator assumes the light sourcehas sent a “0”. The result of this is the equivalent of a 1-bitanalog-to-digital conversion (ADC), at a sampling rate which is equal tothe frame rate of the camera.

After a frame is processed, the results of the ADC conversation arestored in a circular buffer. A sliding correlator is applied to thebuffer to look for the presence of start bits 402. If start bits 402 arefound, the demodulation algorithm assumes it is reading a valid packetof information 401 and proceeds to capture the rest of the transmission.Two samples are used for each bit, so the algorithm creates a linearbuffer that is twice the size of the remaining packet. Each subsequentADC is written sequentially to the linear buffer. When the linear bufferis filled, the demodulation algorithm performs a Fast Fourier Transform(FFT) on the buffer to recover the transmitted signal.

FIG. 3 describes internal components commonly found in LED light source101 with the addition components to allow for the transmission ofoptical signals. The LED light source 101 contains an alternatingcurrent (AC) electrical connection 301 where it connects to an externalpower source, an alternating current to direct current (AC/DC) converter302 which converts the AC signal from the power source into anappropriate DC signal, a modulator 304 which interrupts power to theLEDs in order to turn them on and off, a microcontroller 305 whichcontrols the rate at which the LEDs are modulated, and a LED drivercircuit 303 which provides the appropriate amount of voltage and currentto the LEDs.

Electrical connection 301 is an electrical source that is used to supplypower to the LED light source 101. This most commonly comes in the formof a 120 Volt 60 Hz signal in the United States, and 230 Volt 50 Hz inEurope. While depicted in FIG. 3 as a three pronged outlet, it can alsotake the form of a two terminal Edison socket which the bulb is screwedinto, or a bundle of wires containing a live, neutral, and/or ground.When considering other forms of lighting such as backlighting and accentlighting, the electrical connection can also come in the form of a DCsource instead of an AC source.

Most LED light sources contain an AC/DC converter 302 which converts thealternating current from the power source 301 to a direct current sourceused internally by the components found inside the bulb or light source.The converter takes the alternating current source commonly found inexisting lighting wiring and converts it to a direct current source. LEDlight sources generally use direct current, therefore an AC/DC converteris found in most lighting products regardless of form factor.

LED driver 303 provides the correct amount of current and voltage to theLEDs contained inside the lighting source. This component is commonlyavailable and can have either a constant current or constant voltageoutput. The LEDs found inside most lighting sources are currentcontrolled devices, which require a specific amount of current in orderto operate as designed. This is important for commercial lightingproducts because LEDs change color and luminosity in regards todifferent currents. In order to compensate for this, the LED drivercircuitry is designed to emit a constant amount of current while varyingthe voltage to appropriately compensate for the voltage drops acrosseach LED. Alternatively, there are some high voltage LEDs which requirea constant voltage to maintain their color and luminosity. For thesecases the LED driver circuitry provides a constant voltage while varyingthe current.

Modulator 304 serves the function of modulating the LED light source 101on and off to optically send light 102 signals. The circuits comprisingthe modulator can simply consist of solid state transistors controlledby a digital input. In essence the modulator 304 turns the LEDs on andoff by allowing or preventing current flow. When current flows throughthe modulator with the switches closed the LEDs turn on, and when theswitches are open in the modulator no current can flow and the LEDs turnoff. When the modulator is controlled by an additional logic component,it has the ability to send repeating patterns of on/off signals in orderto transmit digital data through the visible light 102. The modulatorinterfaces directly in between the AC/DC converter 302 and the LEDdriver 303, and is controlled by a microcontroller 305.

The microcontroller 305 provides the digital input signal to themodulator unit 304. This function can also be achieved using a fieldprogrammable gate array (FPGA), but typically consumes more power withadded complexity. The microcontroller's 305 task is to send apredetermined sequence of signals to the modulator 304 which theninterfaces with the LED driver 303 to modulate the outgoing visiblelight from the LED source 101. The microcontroller contains anonvolatile memory storage area, which stores the identification code ofthe light signal. Examples of possible nonvolatile memory sourcesinclude programmable read only memory (PROM), electrically erasableprogrammable read only memory (EEPROM), or Flash.

In regards to the microcontroller pins, the microcontroller 305 containsa digital output pin, which is used to modulate the light output. Togenerate the output signal waveforms, timer modules within themicrocontroller 305 are used. Typical logic levels for the digitaloutput are 3.3V and 5V. This digital output feeds into the modulator 304which interrupts the driver circuit 303 for the LED light source 101.Alternatively, if the LED light source requires lower power, such asbacklighting or individual LED diodes, the output of the microcontroller305 could also be used to drive the light sources directly.

The sequence of signals sent from the microcontroller 305 determines theinformation which is transmitted from the LED light source 101. FIG. 4describes the information 401 format of the optically transmittedinformation from the light 102. At the highest level, each packet ofinformation contains some sort of starting bit sequence, which indicatesthe beginning of a packet, followed by data 403, and some sort of errordetection identifier. The size and position of each portion ofinformation is dependent on the application and is also constrained byrequirements of the receiving device.

Each packet of information 401 transmitted from the LED light source 101contains a sequence of starting bits 402, followed by data 403, and thenterminated with an error detection code 404. Since the LED light sources101 are continually broadcasting information 401, erroneous packets aresimply discarded while the receiver listens for the starting bits 402,indicating the beginning of the next packet. In cases where multiplesources of light are observed by a mobile device 103, multiple pieces ofinformation 401 are received simultaneously.

Information 401 describes the encoded information that is transmitted bythe LED light source 101. The information 401 is contained in a packetstructure with multiple bits which correspond to numeric integer values.The data 403 portion of the information packet can include unique IDcodes 701. Currently the data 403 size is set to 10 bits, but can be ofvarying length. Each bit represents a binary “1” or “0”, with 10 bits ofdata 103 corresponding to 1024 possible values. This corresponds to 1024unique possibilities of ID codes 701 before there is a duplicate. The IDcode can include location information in the ID code that provides ageneral indication of geographical location of the light. Thisgeographical location information can be used to more quickly locatelight source information that is used in determining indoor positioningon the mobile device. For example, the geographical information canpoint to a database to begin searching to find relevant information forpositioning. The geographical information can include existing locationidentifiers such as area code, zip code, census tract, or any othercustomized information.

The ID code 701 is static and is assigned during the calibration phaseof the LED light source 101 during the manufacturing process. One methodto assign the ID code 701 is to place instructions to generate a randomcode in the nonvolatile memory. Once the LED light source 101 is poweredon the microcontroller reads the ID code 701 from the nonvolatile memorystorage area, and then uses this code for broadcasting each and everytime it is subsequently powered on. Since the ID code 701 is static,once it is assigned it will be forever associated locally to thespecific LED light source 101 which contains the microcontroller 305.

FIG. 5 describes the components found in mobile devices 103 that arecapable of receiving optical information. At the highest level themobile device contains an image sensor 501 to capture opticallytransmitted information, a central processing unit 502 to decipher andmanage received information, and a network adapter 503 to send andreceive information.

Photosensors are devices which receive incoming electromagnetic signals,such as light 102, and convert them to electrical signals. In a similarfashion, image sensors are arrays of photosensors which convert opticalimages into electronic signals. The ability to receive signals frommultiple sources is an important benefit when using image sensors forreceiving multiple optical signals.

Image sensor 501 is a typical sensor which is found in most smartdevices. The image sensor converts the incoming optical signal into anelectronic signal. Many devices contain complementarymetal-oxide-semiconductor (CMOS) image sensors, however some still usecharge-coupled devices (CCD). CMOS image sensors are the more popularchoice for mobile devices due to lower manufacturing costs and lowerpower consumption. There are several tradeoffs to consider when choosingan image sensor to perform photogrammetry on multiple LED light sources101. One tradeoff is between the camera resolution and the accuracy ofthe photogrammetric process when triangulating between multiple lightsources—increasing the number of pixels will increase the accuracy.There is also another tradeoff between the data rate of the transmissionand the sampling rate (in frames per second) of the camera. The datarate (in bits/second) is half the frame rate of the camera (e.g., a 30fps camera will receive 15 bps). And finally when determining the lengthof the information 401 packet, the larger the size the longer thereception period, as more bits generally requires longer samplingperiods to capture the full message.

CPU 502 is a generic CPU block found in most smart devices. The CPU 502is in charge of processing received information and sending relevantinformation to the network adapter 503. Additionally the CPU has theability to read and write information to embedded storage 504 within themobile device 103. The CPU 502 can use any standard computerarchitecture. Common architectures for microcontroller devices includeARM and x86.

The network adapter 503 is the networking interface that allows themobile device 103 to connect to cellular and WiFi networks. The networkconnection is used in order for the mobile device 103 to access a datasource containing light ID codes 701 with their corresponding locationdata 702. This can be accomplished without a data connection by storinglocation data 702 locally to the mobile device's 103 internal storage504, but the presence of a network adapter 503 allows for greaterflexibility and decreases the resources needed. Furthermore, the networkadapter 503 is also used to deliver location dependent content to themobile device when it is connected to a larger network 601.

FIG. 6 is a representation of multiple LED sources sending light 102 a-dcontaining identification information 102 to multiple mobile devices 103a-103 b. In this instance the light sources are acting as non-networkedbroadcast beacons; there are no networking modules or physical datawires connecting them. This property is desirable when looking towards acommercial installation of numerous LED light sources 103 a-103 b, asadditional wiring and networking will not be required. However, in orderto receive relevant information the mobile devices have the ability tosend and receive additional information from a local source or a network601. Once the mobile device 103 receives identification information 401from the light sources, it then asks a local or remote source foradditional information.

Enclosed area 602 is a spatial representation of an enclosed roomcontaining four LED sources 101 a-101 d and two mobile devices 103 a-103b, meaning that they can operate next to each other withoutinterference. As a rule of thumb if the received image feed from themobile device sees one or more distinct bright sources of light, it hasthe ability to differentiate and receive the unique information withoutinterference. Because the light capture is based on line of sight,interference is mitigated. In this line of sight environment,interference can arise when the light capture mechanism of the mobiledevice is blocked from the line of sight view of the light source.

Network 601 represents a data network which can be accessed by mobiledevices 103 a-103 b via their embedded network adapters 503. The networkcan consist of a wired or wireless local area network (LAN), with amethod to access a larger wide area network (WAN), or a cellular datanetwork (Edge, 3G, 4G, LTS, etc). The network connection provides theability for the mobile devices 103 a-103 b to send and receiveinformation from additional sources, whether locally or remotely.

FIG. 7 describes how the mobile device 103 receives location data 702.In essence, the mobile device 103 sends decoded ID codes 701 through anetwork 601 to a server 703, which sends back location information 702.The decoded ID codes 701 are found in the information 401, which iscontained in the optically transmitted signal. After receiving thissignal containing a unique ID code 701 the mobile device 103 sends arequest for location data 702 to the server 703, which sends back theappropriate responses. Additionally the request could include othersensor data such as but not limited to GPS coordinates andaccelerometer/gyroscope data, for choosing between different types oflocation data 702 and any additional information.

Location data 702 is the indoor location information which matches thereceived information 401. The location data 702 corresponds to indoorcoordinates which match the ID code 701, similar to how outdoor GPS tagsknown locations of interest with corresponding information. The locationdata 702 could also contain generic data associated with the lightidentification information 401. This could include multimedia content,examples of which include recorded audio, videos, and images. Thelocation data 702 can also vary depending, for example, on othercriteria such as temporal criteria, historical criteria, oruser-specified criteria.

The temporal criteria can include the time of day. The historicalcriteria can include user location history (e.g., locations visitedfrequently), Internet browsing history, retail purchases, or any otherrecorded information about a mobile device user. The user-specifiedcriteria can include policies or rules setup by a user to specify thetype of content they wish to receive or actions the mobile device shouldtake based on location information. For example, the user-specifiedcriteria can include how the mobile device behaves when the user isclose to an item that is on sale. The user may specify that a coupon ispresented to the user, or information about the item is presented on themobile device. The information about the item can include videos,pictures, text, audio, and/or a combination of these that describe orrelate to the item. The item can be something that is for sale, adisplay, a museum piece, or any other physical object.

Server 703 handles incoming ID codes 701, and appropriately returnsindoor location data 702 to the mobile devices 103. The handling canincluding receiving incoming ID codes, searching databases to determinematches, calculating position coordinates based on the ID codes, andcommunicating indoor location data 702. Since the LED light sources 101are acting as “dumb” one way communication beacons, it is up to otherdevices to determine how to use the ID codes to calculate positioninformation and deliver related content. In some embodiments, the server703 can include the information used to link ID codes 701 to physicalspaces and to deliver location-specific content. The server is designedto handle the incoming requests in a scaleable manner, and returnresults to the mobile devices in real-time.

The server can include one or more interfaces to the network that areconfigured to send and receive messages and information in a number ofprotocols such as Internet Protocol (IP) and Transmission ControlProtocol (TCP). The protocols can be arranged in a stack that is used tocommunicate over network 601 to mobile device 103. The server can alsoinclude memory that is configured to store databases and informationused in providing position coordinates and related location basedcontent. The server can include one or more modules that can beimplemented in software or other logic. These modules can performcalculations and perform operations to implement functionality on theserver. The server can use one or more processors to run the modules toperform logical operations.

To describe the server interaction in more detail, FIG. 8 delves intolocation-specific areas 801 containing databases 802 and web services803. The areas 801 represent a subset of databases 802 and web services803 for individual locations where there are installed LED light sources101. The server 703 directly communicates with these installations,which have their own separate sets of information. At a high level,databases 802 represent the stored information pertaining to a specificarea 801, while the web services 803 represent services which allowusers, customers, administrators, and developers access to the ID codes,indoor locations, and other information.

In order to send relevant information, after each received ID code 701,the server 703 requests information pertaining to the specific area 801.Contained in each area 801, are databases which contain informationcorresponding to the specific ID code 701. This information can takemultiple formats, and has the ability to be content specific to avariety of static and dynamic parameters.

In order to optimize response time, the server 703 can constrain itssearch space by using existing positioning technologies available to themobile device 103 or from information in the light source ID codedepending on the embodiment. In essence the server looks for the lightIDs 901 within a specific radius of the current approximate position ofthe mobile device 103, and ignores those that are geographicallyirrelevant. This practice is known as “geo-fencing”, and dramaticallyreduces the request/response time of the server 703. As finalverification, if the database 802 contains one or more of the same IDswithin the current search space that match the ID codes received by themobile device 103 within a specific time frame, then a successfultransaction can be assumed.

As seen in FIG. 9, each database 802 contains numerous sub-categorieswhich store specific types of information. The categories are labeledlight IDs 901, maps 902, content 903, and analytics 904.

Light IDs 901 is a category which contains records of the individuallight ID codes 701 which are contained in an area 801. In a typicallight positioning enabled installation, there will be tens to hundredsof unique LED light sources 101 broadcasting unique ID codes 701. Thepurpose of the light IDs 901 database is to maintain and keep a recordof where the ID codes 701 are physically located in the area 801. Theserecords can come in the form of but are not limited to GPS (latitude,longitude, and altitude) coordinates which are directly mapped into anindoor space. For instance, most indoor facilities have informationabout the number of installed lights, how far apart they are spaced, andhow high the ceilings are. You can then match this information withbuilding floor plans or satellite imagery to create a digital mapping ofwhere each light is positioned.

To expand upon the Light IDs 901 category, additional information cancome in the form of location-specific maps 902. These maps can take onmany physical and digital forms, either directly from the management ofthe location, or a third-party vendor or outside source. In addition tomapping information, location-specific content 903 and analytics 904 arealso contained inside the databases 802.

FIG. 10 is a description of the ID log 1001 information contained in theLight IDs database 901. It is a representation of the file structurethat contains individual records corresponding to individual light IDcodes 701 found within different areas 801. In a typical area 801 thereis a possibility of having duplicate ID codes 701 since there are afinite number of available codes. The size of the ID code 701 isproportional to the length of the data 403 field contained in theoptical information 401.

To deal with duplicate ID codes 701, additional distinguishinginformation can be contained inside of the individual log records; ID 11001, ID 2 1003, and ID 3 1004. This information can contain additionalrecords about neighboring ID codes 701 which are in physical proximityof the LED light source 101, or additional sensor data including but notlimited to: accelerometer or gyroscope data, WiFi triangulation orfingerprinting data, GSM signature data, infrared or Bluetooth data, andultrasonic audio data. Each additional sensor is an input into aBayesian model that maintains an estimation of the current smartphoneposition and the uncertainty associated with the current estimation.Bayesian inference is a statistical method used to calculate degrees ofprobability due to changes in sensory input. In general, greater numbersof sensory inputs correlate with lower uncertainty.

In order to calibrate the light-based positioning system, a userequipped with a specific mobile application will need to walk around thespecific area 801. The mobile application contains map 902 informationof the indoor space, with the positions of the LED light sources 101overlaid on the map. As the user walks around, they will receive IDcodes 701 from the lights. When the user receives an ID code 701, theywill use the map on the mobile app to select which LED light source 101they are under. After the user confirms the selection of the light, themobile application sends a request to the server 703 to update the lightlocation contained in the lighting plan 1103 with the ID code 701.Additional user-provided 1104 metadata including but not limited tocurrent WiFi access points, RSSI, and cellular tower information canalso be included with the server request to update additional databases.

In addition to manual calibration, calibration of LED light source 101locations can also be achieved via crowd-sourcing. In this algorithm, asmobile application users move around an indoor space receiving ID codes701, they will send requests to the server 703 containing the light IDcode 701 received, the current approximate position (based on otherpositioning techniques such as WiFi, GPS, GSM, and inertial sensors) andthe error of the current approximation. Given enough users, machinelearning algorithms on the server 703 can be used to infer the relativeposition of each LED light source 101. The accuracy of this calibrationmethod depends heavily on the number of mobile application users.

FIG. 11 is a description of the maps database 902 and map log 1101information containing floor plans 1102, lighting plans 1103,user-provided information 1104, and aggregated data 1105. Map log 1101is a representation of the file structure that contains the informationfound inside the maps database 902. Information can come in the form ofbut is not limited to computer-aided drafting files, user-providedcomputerized or hand drawn images, or portable document formats. Theinformation residing in the maps 902 database can be used both tocalibrate systems of multiple LED light sources 101, and to augment thelocation data 702 that is sent to mobile devices 103.

Floor plan 1102 contains information about the floor plan for specificareas 801. The contained information can be in the form ofcomputer-aided drafting files, scanned images, and legacy documentspertaining to old floor plans. The information is used to build a modelcorresponding to the most recent building structure and layout. Thesemodels are subject to changes and updates through methods including butnot limited to crowd sourcing models where users update inaccuracies,third-party mapping software updates, and additional input from privatevendors.

Lighting plan 1103 contains information about the physical lightingfixture layout, electrical wiring, and any additional informationregarding the lighting systems in the area 801. This information canalso come in a variety of physical and digital forms such as the floorplan 1102 information. The lighting plan 1103 information is used in thecalibration process of assigning light ID codes 701 to physicalcoordinates within an area 801. In essence, a location with multiple LEDlight sources 101 acts as a large mesh network except, in this case,each node (light ID 701) is a non-networked beacon of information thatdoes not know about its surrounding neighbors. To help make sense ofmultiple light ID codes 701, the lighting plan 1103 information is usedas one of many ways to tell the backend server 703 where LED lightsources 101 are located.

User-provided information 1104 contains additional data that the usermanually uploads in regards to building changes, updates, or newinformation that is acquired. The user in this case is most likely thefacility manager or staff member, but could also originate from an enduser of the system who contributes via a crowd sourcing or machinelearning mechanism. For instance, if an end user was using a light basedpositioning system in a museum and was unable to find a particularexhibit or noticed inaccurate information in regards to location orclassification of the exhibit, they could red flag the occurrence usingtheir mobile device 103. When coupled with data from additional users,sometimes known as a crowd sourcing method, this user-providedinformation 1104 can be used to update and repair inaccuracies in themaps 902 database.

Aggregated data 1105 contains information that is gathered by the systemthat can be used to augment the current information that is known aboutthe mapping environment. This can occur during normal operation of thesystem where multiple mobile devices 103 are constantly sending andreceiving location data 702 from the server 703. Over time theaggregation of this data can be used to better approximate how light IDcodes 701 correspond to the physical locations of the LED light sources101. For instance, if multiple mobile devices 103 consistently receive anew ID code 701, in a repeatable pattern with respect to additionalknown ID codes 701 and other sources of location information, then thisinformation can be recorded and stored in the aggregated data 1105database. This information can additionally be used to recalibrate andin essence “self-heal” a light-based positioning system.

FIG. 12 is a description of the content database 903 and content log1201 information containing static content 1202, user-based content1203, and dynamic content 1204. Content log 1201 is a representation ofthe file structure that contains the information found inside thecontent database 903. Static content 1202 refers to unchanginginformation that is associated with the specific area 801. This canrefer to the previous example where a facility manger loads specificcontent into the content 903 database before a user enters the specificarea 801. This type of information can take the form of but is notlimited to audio recordings, streaming or stored video files, images, orlinks to local or remote websites.

User-based content 1203 refers to content that is dependent on usercriteria. The content can depend on but is not limited to user age, sex,preference, habits, etc. For instance, a male user might receivedifferent advertisements and promotions than a female would.Additionally, age and past purchase habits could also be used todistinguish which is the correct piece of content to be presented to theuser.

Dynamic content 1204 refers to content which changes with varyingfrequency. The content can change dependent on a temporal bases, daily,weekly, monthly, etc. For instance, seasonal marketing and content couldbe automatically presented to the user dependent on the month of theyear, or content in the form of morning, evening, or nightly specialscould be presented numerous times throughout the individual day.

In addition to content, point of purchase 1205 information can bedelivered as well. This could be implemented by using the received IDcode 701 to a secure connection which establishes and completes atransaction linked to a user's selected payment method. Additionally, astandalone point of purchase feature could be implemented by simplylinking ID codes 701 directly to merchandise or services.

FIG. 13 is a description of the analytics database 904 and analytics log1301 information containing frequency 1302, dwell time 1303, path taken1304, and miscellaneous 1305. Analytics log 1101 is the file structurethat contains the information found inside the analytics database 904.Frequency 1302 refers to the number of times each end user visits aparticular location inside of a specific area 801. Separate records aremaintained for individual users, and the frequency is aggregated andsorted in the frequency files database 904.

Dwell time 1303 refers to the time spent in each particular locationinside a specific area 801. Separate records are maintained forindividual users, and the dwell times are aggregated and sorted in thedwell time file. Path taken 1304 refers to the physical path taken by auser in each specific area 801.

Consider an example that combines many of the above descriptions,involving a store owner that installed a light-based indoor positioningsystem and a customer walking around the store using a mobile device 103capable of receiving optically transmitted information. The customerdrives to the parking lot of the store, parks, and walks in. Using thebackground sensors and location services available to her phone asmodeled in FIG. 16, the customer's mobile device 103 already knows thatshe has approached, and most likely entered a store outfitted with alight-based positioning system. Once this information is known, theapplication running on the customer's mobile device 103 initiatesseveral background services and begins to start looking for opticalsignals as depicted in FIG. 15.

Prior to the customer entering the store, the store owner has alreadycalibrated and preloaded the database 802 with the unique LED lightsources 101, map 902 information pertaining to the store floor plan1102, user-provided 1104 product locations, and content 903 in the formof multimedia and local deals in the form of promotions that can only beactivated by visiting that particular section of the store.

In the meantime, the customer is walking around the store looking tofind particular items on her shopping list which she has alreadydigitally loaded onto her mobile device 103. Next, the customer isprompted by her mobile device 103 that one of the items on her list hasmoved locations and an image of the store layout is displayed with aflashing icon indicating where her desired product has moved. The mobilephone can guide her to the new product. Then as soon as she gets closeto the product, an informational video is prompted on her screendetailing the most popular recipe incorporating that product and how itis prepared. Finally, in addition to finding her desired product, thecustomer receives a discount promotion for taking the time to seek outthe new location of the product.

In addition to the services offered by this system to the customer, thestore owner now gains value from learning about the shopping experiencesof the customer. This comes in the form of aggregated data that iscaptured and stored in the analytics 904 section of his store's database802. This example is one of many applications that can be enabled withan accurate indoor light-based positioning system.

FIG. 14 is a process describing the act of receiving location andcontent information through visible light. User places mobile deviceunder light 1401 corresponds to the act of physically placing a cameraequipped mobile device 103 underneath an enabled LED light source 101.The user stands approximately underneath or adjacent the LED lightsource 101, and the mobile device has the LED light source 101 in viewof the camera lens.

The next block, sample image sensor 1402, refers to the act of turningon and reading data from the embedded image sensor in the mobile device103. Receive ID? 1403 is a decision block which either moves forward ifa location ID is received, or returns to sample the image sensor 1402.Get location data corresponding to ID from server 1404 occurs once alocation ID has been received. The mobile device queries the serverasking for location data 702 relevant to the ID code. This describes theprocess of a user obtaining an ID code 701 from a non-networked LEDlight source 101, and using the unique identifier to look up additionalinformation from either the server 703 or a locally stored source.

Finally, content? 1405 is another decision block which determines ifthere is location-based content associated with the received ID code. Ifcontent is available the process continues on to the last block 1406where the content is queried; if not, the process ends. As describedabove, the get content data corresponding to ID from server 1405 refersto the act of retrieving content data associated with a known locationfrom either a server 703 or local source.

FIG. 15 is a process describing the act of turning on the applicationbackground services and determining when to sample the image sensor.Initiate background service 1 1501 is the primary background runningservice on the mobile device. This service is tasked with initiating afunction that can communicate wirelessly to determine if the mobiledevice is close to an enabled area. The wireless communication includesradio frequency communication techniques such as global position system(GPS), cellular communication (e.g., LTE, CDMA, UMTS, GSM), or WiFicommunications. Determine position 1502 is the function thatperiodically samples the wireless communication signal and based ondistance parameters decides whether or not the mobile device is closeenough to an area to move forward to the next service.

Light positioning enabled? 1503 is a decision block that moves forwardif the mobile device is close to an enabled location, or repeats theprevious function if not. Initiate background service 2 1504 isactivated once the mobile device enters an enabled area. The service istasked with initiating the functions that receive location informationvia the modulated light.

Sample ambient light sensor 1505 is the first function of the previousservice which samples the ambient light sensor data as soon as thesensor detects a change. The function of this task is to determine ifthe sensor has gone from dark to light, if the user takes the device outof a pocket or enclosure, or from light to dark, the user has placed thedevice inside of a pocket or enclosure. As an alternative to samplingthe light sensor, the algorithm could also look for a change in theaccelerometer reading. This would correspond to the user taking thephone out of their pocket. Detect change? 1506 is the decision blockthat moves forward if the ambient light sensor has gone from dark tolight, meaning that the mobile device is potentially in view ofsurrounding modulated light.

FIG. 16 is a process describing the act of determining a mobile device'sposition using a variety of information sources. Sample GPS/GSM 1601refers to the act of determining if the mobile device is close to anenabled area. Enabled area? 1602 is a decision block which moves forwardif the mobile device is close to a enabled area, or returns to theprevious block if not.

Sample alternative sources 1603 refers to the act of leveraging existingalternative positioning technologies such as WiFi, Bluetooth,ultrasound, inertial navigation, or employing an existing service usingone or more of any available services. Record internal sensor data 1606is a task which records the current accelerometer data for a period oftime before returning to the Sample image sensor 1402 block. This taskis performed so that location information is constantly being collectedeven when modulated light is not being detected. This allows the mobiledevice and/or server to keep track of the mobile device's position.

FIG. 17 is a system diagram describing how a client device 1704interacts with a light-based positioning system 1709. Network 601 is ageneric local or remote network used to connect mobile devices 103contained in locations A 1701, B 1702, and C 1703 with the light-basedpositioning service 1709.

Each location contains multiple LED light sources 101, each of whichbroadcast unique identification codes 701. In order to interact with thesystem from an operator's perspective, a mobile device can use thedatabase service application 1710 which contains multiple privilegelevels for different levels of access. The client privilege leveldetermines read/write permissions to each of these databases. Theselevels include users 1705 which refer to general front end system users,administrators 1706 which are usually IT or operations management levelwithin an installation, developers 1707 which have access to theapplication programming interfaces of the system for use in customapplication development, and root 1708 level which contains mastercontrol over the users and access to everything contained in the systemand databases.

Mobile devices in each location 1701, 1702, and 1703 receiveidentification codes 701 from lights in their respective locations. Theythen send the received identification codes 701 through the network 601which connects to database service application 1710, through userapplication 1705, and has read access to maps 902 and content, and writeaccess to analytics 904. A generic client, 1704, connects to databaseservice application 1710 through network connection 601.

The client uses a password authorized login screen to access therespective permission status. Clients with administrator permissionshave read/write access to light IDs 901, read access to maps 902,read/write access to content 903, and read access to analytics 904.Clients with developer permissions 1707 have read access to light IDs,read access to maps 902, read/write access to content 903, and readaccess to analytics 904. A client with root permissions 1708 hasread/write access to databases 901-904.

As an overview, FIG. 17 describes the top down approach to our currentimplementation of a light-based positioning system. At the highestlevel, known locations of installed non-network standalone LED lightsources 101 are used to accurately identify the relative position ofmobile devices 103. In order to obtain identification information fromthe lights, the background processes running on the mobile device 103have been described in FIGS. 14, 15, 16. Once the mobile device hasacquired a unique or semi-unique ID code 701 from the light orcombination of lights, it uses this information to query a database 802for additional information. This information can come in many forms, andis used to create a more personalized experience for the user. Asinitially mentioned, this local experience is used for location awaremobile computing, and augmented reality applications. In addition tolocal personalized information, location based analytics applicationscan be enabled from the aggregated data and traffic running through theserver 703.

The use of light-based positioning capabilities provide a number ofbenefits. For example, the positioning information obtained by usinglight sources is highly precise compared to alternative techniques forpositioning information. The accuracy of a light-based positioningsystem can be down to a few centimeters in three dimensions in someembodiments. This positioning ability enables a number of usefulservices to be provided. In certain embodiments, additional mobiledevice information can be used in combination with the positioninginformation. For example, accelerometer position information can be usedin conjunction with light source based position to offer augmentedreality or location aware content that relevant to the device'sposition. The relevant content can be displayed to augment what is beingdisplayed on the mobile device or the display can provide relevantinformation. Applications on the mobile device can also be launched whenthe mobile device enters certain areas or based on a combination ofcriteria and position information. The applications can be used toprovide additional information to the user of the mobile device.

The light-based positioning systems and methods can also be used tomanage and run a business. For example, the light-based positioning canhelp keep track of inventory and to make changes to related databases ofinformation. In a warehouse, for example, the light-positioning systemcan direct a person to where a particular item is located by givingdirections and visual aids. The light positioning can even providepositioning information to direct the person to the correct shelf theitem is currently residing on. If the person removes the item, themobile device can update the inventory databases to reflect the change.The same function can be implemented in a store environment asmerchandise locations are changed or updated. This information can thenbe used in providing content to a user. For example, if a shopper wantsmore information about an item, the updated location can be used tolocate the item or direct the shopper to an online website to purchasean out-of-stock item. In some embodiments, the mobile device using thelight-based positioning technique in conjunction with a wirelessconnection and other information can be used to provide non-intrusivedata collection on customers. The data collection of how customers movethrough a store and where they spend time can be used to improve layoutof stores and displays of merchandise.

The light-based positioning systems are also easy and low cost to setupcompared to other location positioning systems. Since each light sourceoperates autonomously, a building owner only needs to swap out existinglight sources for those that provide light-based information to a cameraenabled device. The light sources are non-networked independent beaconsthat broadcast identification codes configured when manufactured. Thisallows the light sources to be manufactured at a lower cost compared tonetworked light sources. Further, the non-networked independent beaconlight sources in the light-based positioning system can be easier forbuilding owners to install.

The light-based positioning system can also include optimizations insome embodiments. For example, location information obtained from eitherthe identification code or from alternative techniques can be used toreduce latency in determining position information. This optimizationcan work through geo-fencing by constraining the search area to findinformation regarding the captured light sources more quickly. This canreduce the overall delay experienced by a user from the time the mobiledevice captures the light sources to when relevant position informationis provide to the mobile device and/or relevant content is provided tothe mobile device.

Efficient Light Bulbs for DPR Schemes

One of the biggest challenges facing beacon based light positioningsystems is managing the additional power consumption ofcommunication-enabled lighting devices in comparison to that ofnon-communicating devices. Lighting sources 101 in general, regardlessof form factor or technology, are differentiated in part by their powerconsumption; generally, the less the better. Accordingly, higher energyefficiency is one of the core economic forces driving adoption ofLight-Emitting-Diodes (LEDs). However, when using light sources 101 as ameans for communication devices, the power requirements tend to increasedepending on the modulation scheme since energy must be divided betweenthe carrier wave and the modulation wave. There are many differenttechniques for transmitting data through light, as discussed in PriorArt such as U.S. Ser. No. 12/412,515 and U.S. Ser. No. 11/998,286, andU.S. Ser. No. 11/591,677. However, these techniques have majorly beenpursued without considering their impact on light source 101 parameters,including efficacy, lifetime, and brightness. Since light sources 101are first and foremost illumination devices, and not communicationdevices, the communication function takes a secondary role. The presentdisclosure utilizes Digital Pulse Recognition (DPR) modulation as atechnique for transmitting data while minimizing the impact onillumination devices.

FIGS. 18A-C represent several digitally modulated light sources 101 a-cwith varying duty cycles; a low duty cycle 1801, a medium duty cycle1802, and a high duty cycle 1803. A duty cycle is a property of adigital signal that represents the proportion of time the signal spendsin an active, or “on,” state as opposed to an inactive, or “off,” state.A light source with a low duty cycle 1801 is inactive for a highproportion of time. A light source with a medium duty cycle 1802 isinactive for about the same proportion of time that it is active. Alight source with a high duty cycle 1803 is active for a high proportionof time. The duty cycle of a light source affects the luminosity of thelight source. A light source having a higher duty cycle generallyprovides more luminosity than that same light source with a lower dutycycle because it is on for a higher proportion of time. Duty cycle isone aspect of a modulation scheme. Other aspects include pulse shape,frequency of pulses, and an offset level (e.g., a DC bias).

Because DPR modulated light sources 101 rely on frequency modulation,they are able to circumvent the limitations of traditional AM basedapproaches. Note that frequency modulation in this context does notrefer to modifying the frequency of the carrier (which is the lightsignal), but instead to modifying the frequency of a periodic waveformdriving the light source. One popular technique for dimming LED lightsources 101 is with pulse width modulation (PWM), which controls theaverage power delivered to the light source by varying the duty cycle ofa pulse. In a DPR modulation system utilizing PWM, a DPR modulator wouldcontrol the frequency of the pulses, with the duty cycle determined bythe dimming requirements on the light source 101. As used herein, a DPRmodulated light source, having a DPR modulation frequency, refers to alight source having an output modulated in such a manner that a receiverusing DPR demodulation techniques can demodulate the signal to extractdata from the signal. In some embodiments, the data can includeinformation in the form of an identifier which distinguishes a lightsource from other nearby DPR modulated light sources. In someembodiments, this identifier may be a periodic tone that the lightsource randomly selects to identify itself. A periodic tone may be asignal that repeats with a given frequency. In other embodiments, alight source may receive such an identifier from an external source.

To determine the maximum duty cycle (D) supported by DPR demodulation,the modulation frequency (f) of the transmitter and the sampling timefor the image sensor (T_(s)) of the receiver are first defined. Next theduty cycle parameters (T_(off)) and (T_(on)) are defined whichcorrespond to the on and off times of the light source. T_(s) is animportant parameter because the image sensor sampling time defines aminimum amount of modulation time required to produce the bandingeffects which allow for the frequency detection required for DPRdemodulation. The required modulation time can refer to either theT_(on) portion 1804 or the T_(off) portion 1805 of the signal, howeverto maximize the brightness of the light source T_(off) is used as thelimiting variable (if solving for the minimum duty cycle, T_(on) can beused). If T_(s) of the receiving device is less than twice T_(off) ofthe light source, residual banding on the image sensor will not takeplace; therefore the signal cannot be extracted. In order for banding tooccur, T_(s) should be greater than twice the value of T_(off)(T_(s)>2*T_(off)).

It is important to note that when designing for the maximum duty cycle,the modulation frequency can be defined from the transmitter side andcan be completely independent of the sampling time T_(s). This isbecause the sampling frequency T_(s) is a property of the receiver whichis defined by the image sensor manufacturer and is likely not designedfor optimal DPR demodulation properties. T_(s) varies depending on thespecific image sensor, and can be expected to change as more advancedimage sensors are developed. Therefore it is important to optimize suchthat a broad range of both modulation and sampling frequencies can beused. In the next sections the equations and variables for how tocalculate the maximum duty cycle are described for a variety of testcases.

In order to solve for T_(off) in terms of duty cycle and modulationfrequency, one can first start with the fundamental definition of whatthe duty cycle is: 1 minus the ratio of signal on time divided by thecombination of signal on and off time. In the case of a modulated lightsource, D=1−T_(off)/(T_(on)+T_(off)). Next the modulation frequency (f)can be defined as the inverse of the sum of signal on and off times:f=1/(T_(on)+T_(off)). Substituting f into the previous equation for Dyields D=1−f*T_(off). The variable T_(off), which was previously definedas a value less than twice T_(s), can then be used to define the maximumduty cycle for any given modulation used in DPR demodulation. Afterrearranging and substituting T_(s) for T_(off) (T_(off)<0.5*T_(s)),D=1−f*(½)*(T_(s)). With this equation, we can now solve for the maximumduty cycle achievable given the modulation frequency of the transmitter,and the sampling time of the receiver.

Since the maximum duty cycle is dependent on both the modulationfrequency of the transmitter and the sampling frequency (F_(s)=1/T_(s))of the receiver, its exact percentage value can change depending on thepresent conditions. For testing purposes, the modulation frequency rangewas chosen to start at 300 Hz which is above the range which the humaneye can see. The modulation frequency range may range from 60 Hz to 5000Hz. Typical image sensor sampling frequencies (F_(s)=1/L) range between20 kHz and 36 kHz for high quality image settings (640 by 480 pixelresolution), and 4 KHz to 7 KHz for low quality image settings (192 by144 pixel resolution). In some embodiments, the image sensor samplingfrequencies may range from as low as 1 KHz to as high as 1 MHz.

When analyzing specific use cases, the duty cycles corresponding to amodulation frequency of 300 Hz and sampling frequencies for high qualityimage settings in some embodiments result in D=1−(300 Hz)*(½)*( 1/20Khz)=99.25% and D=1−(300 Hz)*(½)( 1/36 kHz)=99.58%. The duty cyclescorresponding to a modulation frequency of 300 Hz and typical samplingfrequencies low quality sampling frequencies in other embodiments resultin D=1−(300 Hz)*(½)*(¼ kHz)=96.25% and D=1−(300 Hz)*(½)*( 1/7kHz)=97.86%. In yet other embodiments, a 2000 Hz modulation frequencyand high quality sampling frequencies of 20 kHz and 36 kHz results inD=95.00% and 97.22% respectively, and for low quality samplingfrequencies of 4 kHz and 7 kHz results in D=75% and 85.71% respectively.

After the maximum duty cycle has been calculated, to compensate for theadditional power requirements needed for data communication due to theoff portion 1804 of the modulation signal, the input power can beincreased such that the resulting average power of the communicatinglight source 101 is identical to the non-communicating light source 101.In effect the average power of the two light sources will be the same,yielding a perceivably identical luminous output. Take for instance LEDsource “A” which is powered by 6 Watts and modulated where 50% of thetime it is “on”, and the remaining 50% “off”, effectively resulting in a3 Watt average power. In order for this light source 101 to match theluminous output of the 6 Watt LED source “B” which is not modulating andis on 100% of the time, one can double the input power from 6 Watts to12 Watts. While the input power of “A” was increased, the average poweris halved to equal 6 Watts; therefore sources “A” and “B” appear to beidentical to the human eye in terms of brightness.

However, there exists a point where increasing the input power candecrease the efficiency of a given light source 101. For LED lightingdevices it is important to stay within the manufacturer specifiedvoltage and more importantly current, otherwise efficiency drasticallyfalls with increased supply current. This unwanted effect is known asLED “droop”, and generally refers to decreased luminous output for anygiven individual LED (assuming one or more LEDs per lighting source 101)due to the additional thermal heating resulting from the increasedcurrent. In the previous example, the input power to LED source “A” wasdoubled while the input power to “B” was left unchanged. Assuming thateach source was supplied by a constant 12 Volts, this means that theinput current to source “A” had to have doubled in order to achieve therequired 12 Watts of power consumption. This equates to a 50% increasein current, when moving from 0.5 Amps to 1 Amp, and can only beperformed if within the manufacturers' tolerable input current range forthe LEDs.

Given inputs of drive current (Id) and operating voltage (V), we candefine the power (P) of a non-modulated light source 101 as P=Id*V, andcompare it with the additional required power (P_(mod)) of a modulatedlight source 101. To define the additional power needed due tomodulation, you can then define the relationship as P_(mod)=P2−(D*Id*V).While the input variables used in this example vary from source tosource, this method can be used to accommodate for power loss due tomodulation.

We can now solve for the power required to support the maximum dutycycles that were previously solved for. In this example, the powerconsumed by the non-modulated light source equals P=Id*V=700 mA*12V=8.4W. P_(mod) can then be calculated to describe how much extra power isrequired to support a modulated light source 101 with regard to the dutycycle. Recall that for a modulation frequency of 2000 Hz and samplingfrequencies of 20 kHz and 4 kHz, the maximum duty cycle equaled 99.25%and 96.25%. Therefore the additional power needed to detect a 2000 Hzsignal at a sampling frequency of 20 kHz is defined as Pmod=8.4W−(0.9925*700 mA*12V)=63 mW, a 0.75% increase in required power on topof the baseline 8.4 W. For 2000 Hz at a sampling rate of 4 kHz,P_(mod)=8.4−(0.9625*700 mA*12V)=315 mW, a 3.75% increase in requiredpower.

While finding the maximum duty cycle supported by DPR demodulation isimportant for maintaining the brightest luminous output levels, it isalso important to support the lowest duty cycle possible in order tosupport the dimmest luminous output levels. This is because the minimumduty cycle corresponds to the dimmest level that a modulated lightsource 101 can operate at while still supporting DPR demodulation from areceiving device. In order to account for this, we now consider theT_(on) portion of the signal rather than T_(off). The limiting samplingfactor now changes to require that T_(s) is greater than twice T_(on)(T_(s)>2T_(on)). Substituting this condition into the previous max dutycycle equation (replacing {1−D} with D), the resulting equation yieldsD=(½)*f*T_(s).

Repeating the above examples for a modulation frequency of 300 Hz andhigh quality sampling frequencies (1/T_(s)) of 20 kHz and 36 kHz,D=0.75% and 0.42%, respectively. For a modulation frequency of 2000 Hzwith high quality sampling frequencies, D=5.00% and 2.78%. Consideringlower quality sampling frequencies at 300 Hz and 2000 Hz, D=3.75% and2.14% for a 300 Hz modulation frequency, and D=25.00% and 14.29% for a2000 Hz modulation frequency.

In addition to modifying the overall duty cycle, there also exists theopportunity to tune the modulation scheme such that during the “off”portion 1805 of operation the light source 101 does not turn completelyoff. As described in FIGS. 19A-C, modulation schemes 1901, 1902, and1903 depict varying duty cycles where a DC bias 1904 has been addedwhich correspond to the modulated light sources 101 a-101 c. Modulationschemes where the light source 101 does not turn all the way “off” areimportant when considering light source 101 brightness, efficiency,lifetime, and the signal to noise ratio (SNR) of the communicationschannel. The DC bias 1904 during modulation reduces the peak powerrequired to drive the light source for a given brightness. A reductionin peak power will reduce the negative impact of overdriving thelighting source, which is known to cause efficiency losses known as“droop” for LEDs, in addition to decreasing light source 101 lifetimes.

As an example, consider that the average power delivered to the lightsource is defined as: P_(av)D*P_(on)+(1−D)*P_(off), where D is the dutycycle and P_(on), P_(off) are the respective on/off powers. The impacton light source 101 brightness is that increasing the “off” power willincrease the total power. This reduces the required peak power deliveredto the lighting source, because the power transferred during the “off”period can make up the difference. In a system operating at a duty cycleof 50%, for a fixed brightness B, a 10% increase in the “off” periodpower translates to a 10% decrease in the “on” period power.

When approaching the above power equation from a constant voltage (V),average current (I_(av)), and on/off current (I_(on)/I_(off)) standpoint(P=IV), I_(av)*V=D*I_(on)*V+(1−D)*I_(off)*V. After removing the constantV, I_(av)D*I_(on)+(1−D)I_(off). For example, in the case of a lightsource 101 requiring an average drive current (I_(ave)) of 700 mA andoff current of (I_(off)) of 0 A undergoing modulation with a duty cycle(D) of 96.25%, the peak current (I_(on)) requirement is I_(on)=700mA/0.9625=727 mA. If instead the current delivered during the “off” timeis 100 mA the average current reduces to I_(av)=0.9625*700mA+(1−0.9625)*100 mA=678 mA, a 6.7% decrease in overall required powergiven constant voltage. In other embodiments, a constant current may beapplied with differing voltages to achieve a similar effect.

The impact of non-zero I_(off) values for the previous example istwo-fold. First, a reduction in required power is achieved, and secondincreasing the “off” time power lowers the required duty cycle toachieve a fixed brightness level. For the previous example when solvingfor D, D=(I_(av)−I_(off))/(I_(on)−I_(off)). The difference in duty cyclecan now be determined for the reduction in peak current from 727 mA to678 mA, as D=(700 mA−100 mA)/(727 mA−100 mA)=95.69%, which is a 0.56%difference from 96.25%. This essentially allows for a brighter lightsource 101 with a decreased duty cycle, and lower power requirements.

Another major requirement for DPR modulation is to interface withexisting light dimmers. There are a variety of light source 101 dimmersemployed on the commercial market. One popular dimming technique istriac dimming. In a triac dimmer, a variable resistor switch is used tocontrol the amount of power delivered to the light source 101 over theAC line. For traditional incandescent and fluorescent sources this is acost effective and efficient way to control the power, and thus thebrightness, delivered to the light source 101. For LED light sources101, it is necessary to put a special driver between the triac dimmingcircuit and the LED source. This is because LEDs are current drivendevices, and thus require an AC/DC converter to transform AC from thepower lines to a DC current for driving the LEDs.

FIG. 20 demonstrates a system by which a DPR modulator can interfacewith existing lighting control circuits. A dimmer controller 2002 sendsa dimmer signal 2003 to a dimmable LED driver 2006. In the case of anLED light source controlled by a triac dimmer, the dimmer signal wouldbe transmitted across the AC power line. The dimmable LED driver 2006then converts the dimmer signal to a pulse width modulated signal usedfor driving the light output 2007 of the source 2001. The configurationof the system diagram shows the dimmer signal 2003 going to both the DPRmodulator 2004 and the LED driver 2006, however this does not alwaysneed to happen. In some instances the LED driver 2006 can contain a“master override” input which is designed to supersede any dimmer signal2003 input. In this case the dimmer signal 2003 still goes to the LEDdriver 2006, but is ignored. In other cases where there is not anoverride input, the dimming signal only goes to the DPR modulator.

DPR modulator 2004 is responsible for sending DPR signals 2005 to theLED driver 2006 which controls the light output 2007. In the case of thelight source 2001 being driven by pulse width modulation as the dimmersignal 2003 from the dimmer controller 2002, DPR modulator 2004 controlsthe frequency of the PWM signal and selects the desired value. The widthof pulses in signals 1801-1803 are determined based on dimmer signal2003, which indicates the desired light source 2001 brightness level.Note that the dimmer controller 2002 is not contained within the lightsource 2001, and can output a variety of dimmer signals 2003 (triac, ora proprietary method). Because of this, the DPR modulator 2004 isresponsible for interpreting these different signals and appropriatelyoutputting a DPR signal 2005 which corresponds to the desired brightnesslevel of the inputted dimmer signal 2003. In cases where dimming is notrequired and the dimmer signal 2003 is not present, the DPR modulator2004 interfaces directly with the LED driver. In some implementations,the DPR modulator 2004 can also be contained inside the LED driver 2006as part of an integrated solution instead of as a separate component.

FIG. 21 contains a high level overview of a DPR modulator 2004. Data2101 is first sent to DPR tone generator 2102. Data 2101 could containinformation from any source. In the context of a beacon based lightpositioning system, data can include the identifier for the light. DPRtone generator 2102 converts the data 2101 into a sequence of DPR tones.A DPR tone is a periodic digital signal that oscillates between activeand inactive states with a particular frequency. This process isdescribed further in FIG. 22. Depending on the requirements of the datatransmission channel, this could either be a single tone (suitable for abeacon based positioning system using light identifiers), or a sequenceof tones (if higher data rates are desired by the end user). The DPRTone(s) 2203 are then sent to the waveform generator 2103, which isresponsible for generating the DPR signal 2005 for driving the LEDs.Waveform generator 2103 receives a dimmer signal 2003 input from adimmer controller 2002, which controls the brightness of the lightsource. In the case of a DPR tone as a pulse-width-modulated signal,dimmer controller 2002 would control the duty cycle of square wave 1802,while DPR Tone(s) 2203 would control the frequency of the square wave.The result is an output DPR signal 2005, which is then sent to the LEDdriver 2006.

FIG. 22 contains a breakdown of DPR Tone Generator 2102. This module isresponsible for taking a piece of data and converting it to a sequenceof DPR tones. A DPR tone determines the frequency at which a waveform,such as the square waves from FIG. 18, is sent. The range of possibletones, defined here in as T_(o) through T_(n), is determined by both thesampling time, T_(s), of the image sensor (as discussed in paragraph0006), and the frequency response of the light source 101. Encoder 2201is a standard base converter—it takes a piece of data in binary andconverts it into a corresponding DPR tone. A typical range for tonescreated by DPR Tone Generator 2102 is 300 Hz-2000 Hz, in steps of 10 Hz,allowing for 170 distinct DPR tones. The step size between tones isselected to reduce noise, and depending on the requirements could bemuch higher or lower than 10 Hz. As an example, that data 2101 maycontain an identifier of value 10 for light source 101. This identifieris passed to Tone(s) Generator 2102, which generates (or selects frommemory) a sequence of tones. Note that the length of a DPR tone sequencecould be as low as 1 (in the case of a single tone used in a beaconbased positioning system). In this example, an identifier of 10 wouldmap to a DPR tone of 400 Hz. DPR Tone Generator 2102 could either storethe identifier in memory beforehand, using pre-computed mappings of datato tone sequences, or alternatively it could compute this on the fly.The exact method of generating the sequence of tones would be driven bythe resources available on the light source 101. Once one of thepossible tones sequences 2202 is created, it is sent to WaveformGenerator 2103.

FIG. 23 contains the breakdown of Waveform Generator system 2103, whichcombines a tone sequence 2202 with a waveform from symbol creator 2303and dimmer signal 2003 to create a DPR signal 2005 for driving lightsource 101. The resulting waveform will be periodic, with a frequencydefined by the sequence of tones, a symbol created based on the list ofpossible symbols in symbol creator 2303, and an average output(brightness) determined by the dimmer signal 2003. This desiredbrightness could either be hard-coded on the module, or provided as anexternal input through a dimming control module. The choice of a symbolis determined within Symbol Selector 2301, which generates a controlline 2302 for selecting a symbol from symbol mux 2402.

FIG. 24 contains the breakdown of Symbol Creator 2303, which holdspossible symbols 2401 a-2401 d. These could include a saw tooth wave2401 a, sine wave 2401 b, square wave 2401 c, and square wave with a DCoffset 2401 d, or any other periodic symbol. Symbol creator then takesin a selected symbol 2402, and modifies it such that a desiredbrightness 2106 is achieved. In the case of a square wave symbol 2401 c,dimmer signal 2003 would modify the duty cycle of the square wave. Theresulting waveform is then sent to output signal 2005 for driving thelight source.

The goal of the output waveform 2105, which drives light source 101, isto illuminate a scene in such a way that the DPR modulated signal can bepicked up on any standard mobile device 103. Reducing flicker on videowhich is under illumination from fluorescent lamps is a well-knownproblem. The flicker is caused by periodic voltage fluctuations on theAC line powering the lamp. For a lamp powered by a 50 Hz AC line, theluminance level changes at 100 Hz. This causes alternating white/darkbands to appear in video recorded with CMOS imagers. The bands are aresult of the rolling shutter mechanism on CMOS imagers, which partiallyexpose different areas of the image at different points in time. Thelines on the image can occur on both, one, or on multiple frames, andmay appear to move in time. See, for example, U.S. Pat. No. 6,710,818,the entire contents of which is hereby incorporated in its entirety,which describes methods for detecting and removing this unwanted effect.Possible algorithms for mitigating flicker include automatic exposurecontrol, automatic gain control, and anti-banding. These techniques arecommon in many mobile devices as a means to remove flicker caused byfluorescent lamps.

Advanced DPR Demodulation Techniques

DPR demodulation, instead of removing flicker, exploits the rollingshutter effects of CMOS cameras as a means of transmitting data. A CMOSdevice with a rolling shutter captures an image frame by sequentiallycapturing portions of the frame on a rolling, or time-separated, basis.These portions may be vertical or horizontal lines or “stripes” of theimage that are captured at successive time intervals. Because not everystripe is captured in the same time interval, the light sourcesilluminating the image may be in different states at each of these timeintervals. Accordingly, a light source may produce stripes in a capturedframe if it is illuminated in some time intervals and not illuminated inother time intervals. Light sources that broadcast digital pulserecognition signals may produce patterns of stripes. Since the patternof stripes is dependent on the frequency of the digital pulserecognition signal, and the speed of the rolling shutter can bedetermined a-priori, image processing techniques can be used to deducethe illumination frequency based on the width of the stripes. Forexample, consider a room containing five light sources 101, eachbroadcasting at 500 Hz, 600 Hz, 700 Hz, 800 Hz, and 900 Hz,respectively. Each distinct frequency, otherwise known as a DPR tone,can be used to identify the light source 101. In a beacon based lightpositioning system, a mobile device receiver within view of thetransmitting lights can detect the DPR tones, correlate an identifierassociated with the tone, and then use a lookup table to determine thelocation of the device based on the location associated with theidentifier(s).

Modeling the camera sampling function is essential to understanding howDPR demodulation works on modern image sensors, and how the impacts ofvarious hardware-dependent parameters affect the DPR signal 2105. Torepresent this, FIG. 25 is a continuous time representation 2501 of howan individual row on a rolling shutter image sensor is sampled. Theexposure time interval 2502 represents the period over which lightaccumulates on the photo sensor. If the exposure time is much lower thanthe period of the DPR modulated signal, the light and dark bands will beclearly defined. If the exposure time is longer, the light and darkbands will lose their definition.

FIG. 26 contains a continuous time example 2601 of a DPR modulated lightsignal. In this example, the signal is a square wave with a 50% dutycycle being driven at a DPR tone of 300 Hz. The relationship between theDPR illumination period 2602 and the exposure time 2502 determines howwell defined the bands are on the received image.

FIG. 27 is the continuous time sampled image 2701, created by convolvingan individual row sampling function 2501 with a DPR modulated signal2601. The alternating periods of high brightness 2702 and low brightness2803 are caused by the DPR modulation frequency, and appear asalternating white/dark bands on the received image.

FIG. 28 is a representation of a discrete time domain signal model 2801for representing how a rolling shutter on an image sensor samples theincoming light pulses 2601. The rolling shutter is modeled as an impulsetrain, containing a sequence of the Dirac Delta functions (otherwiseknown as a Dirac comb). Each impulse is separated by an interval, T,which corresponds to the speed of the rolling shutter commonly found inmost CMOS image sensors. The interval T varies from device to devicewhich causes the bands on scenes illuminated by DPR modulated signals tovary in size. The mobile device 103 needs to account for hardwaredependent factors (rolling shutter speed) to properly determine the DPRtone. FIG. 29 contains a discrete time representation 2901 of therolling shutter sampling functionality over multiple frames.

Because rolling shutter speeds are typically faster than frame rates,DPR demodulation on current imaging technology is capable of much higherdata rates than modulation schemes that sample on a per frame basis. Ina DPR modulated system using a 640×480 pixel image sensor, the sensorwould capture 480 samples per frame (represented as 480 consecutivedelta functions in sensor model 2801). A demodulation scheme using aglobal shutter would only be capable of taking one sample per frame.This is a key advantage for indoor positioning using beacon basedbroadcasting schemes because the time-to-first-fix is orders ofmagnitude faster than competing technology, which can take severalseconds to receive a signal. For example, consider a typical mobiledevice 103 camera which samples at 30 frames per second (FPS). Using DPRdemodulation, time-to-first-fix can be achieved with as little as asingle frame, or 1/30 of a second, versus 1 second for a demodulationscheme that samples on a per frame basis. This compares to atime-to-first-fix of up to 65 seconds for GPS, 30 seconds for assistedGPS, and 5-10 seconds for WiFi positioning.

This order of magnitude improvement opens the door for applications inwhich latency for time-to-first-fix must be minimized. Furthermore,computation for DPR demodulation can be performed on the mobile deviceitself, versus the server side processing required for WiFifingerprinting algorithms. In a mobile environment, where connection toa network is not guaranteed, client side processing provides a majoradvantage. In the future, it is expected that image sensors will havemuch higher frame rates. In this scenario, DPR demodulation can beadjusted to sample on a per-frame basis, instead of a rolling shutterbasis. The key principle is that the demodulator can be adjusted insoftware, allowing future mobile devices to tune their receivingcharacteristics to receive DPR signals. The software adjustments thatneed to be applied are the subject of the following sections.

Configuring a Device for DPR Demodulation

In order to prepare a mobile device 103 to receive the modulated DPRsignals 2105, the device must first be configured. This is to counteractthe flicker mitigation algorithms typically applied in mobile deviceimage sensors. FIG. 30 describes the method by which mobile device 103is configured to receive DPR modulated signals. First, the initializesensors 3001 function initializes and activates the available sensorscapable of receiving data. For typical modern mobile devices these wouldinclude both the front and rear facing cameras. Determine sensors tomodify 3002 then decides which sensors need to be modified. A number ofpossible factors determine whether or not a particular sensor should beinitialized then modified, including power consumption, accuracy, timesince last reading, environmental conditions, required locationaccuracy, and battery state.

Modify sensors 3003 then passes a list of the appropriate sensors whichneed to be modified to a function which has additional information aboutthe mobile device 103 and adjusts the demodulation scheme for devicespecific limitations 3004. In the case of using an embedded mobiledevice 103 camera to demodulate DPR signals, possible sensor parametersto modify include exposure, focus, saturation, white balance, zoom,contrast, brightness, gain, sharpness, ISO, resolution, image quality,scene selection, and metering mode. As part of the modification step3003, sensor parameters such as exposure, white-balance, and focus arelocked to prevent further adjustments.

After the sensors are modified 3003, specific hardware limitations areadjusted for in the demodulation scheme by using a device profile. Themost important of these is the rolling shutter speed. Because differentmodels of mobile device 103 will, in general, have different camerasensors, the line width of the DPR tone measure on an image sensor willvary across hardware platforms for a fixed frequency. For this reason,it is necessary to adjust the stripe width one is looking for dependingon the specific characteristics of the device. In the Fourier Techniquesdiscussed later on in the application, modifying the stripe widthcorresponds to modifying the sampling frequency of Dirac Comb 2801.

There are a number of challenges associated with controlling the cameraparameters to optimize for DPR demodulation. One challenge is overridingthe automatic parameter adjustments that mobile operating systemstypically provide as part of their camera application programminginterfaces (APIs). In the case of an embedded image sensor, the sensorsettings are adjusted automatically depending on factors such as but notlimited to ambient light conditions, areas of focus, distance fromobjects, and predetermined scene selection modes. For instance, whentaking a picture with an image sensor, if the scene is dark then theexposure time is automatically increased. When taking picture of a scenemode with fast moving objects, the exposure time is usually decreased.

When using an image sensor for DPR demodulation, these automaticadjustments can introduce noise into the signal, causing higher errorrates. Specifically in the case of exposure, longer exposure timescorrespond to lower data rates, which correspond to a decreased amountof available light IDs 901. At the edge case, if the exposure time issufficiently long, then the sampling rate will drop so low that DPRdemodulation becomes extremely challenging as the signal is severelyunder sampled. Furthermore, if the camera is constantly adjusting, thenthe performance of background subtraction (discussed later), whichisolates the moving stripes from the rest of the picture, will besignificantly impaired. This is because the automatic adjustments areconstantly changing the pixel values. In order to successfully transmitDPR signals, these automatic adjustments need to be accounted for.

Practically speaking, many mobile device 103 APIs do not allow for themodification of sensor parameters in the top level software. Theproposed method in FIG. 31 describes a method for working around theprovided APIs to control the exposure. Current API's do not allow formanual exposure control, so instead of manually setting the exposure, wepresent an algorithm that exploits the metering functionality tominimize the exposure time.

FIG. 31 contains a process for modifying the various sensor parameterscontained in a mobile device 103 in a way that overcomes the limitationsimposed by current camera APIs. In the algorithm, the first step is toinitialize the required sensors 3001. For the case of an image sensor,this involves setting the frame rate, data format, encoding scheme, andcolor space for the required sensors. After the image sensors have beeninitialized 3001, the algorithm searches for regions of interest 3101.In the case of setting the exposure using metering, these regions ofinterest 3101 would be the brightest regions of the image. Set meteringarea 3102 then sets the metering area to the brightest portion,effectively “tricking” the mobile device 103 into lowering the exposuretime. Lock parameter 3103 then locks this exposure time to prevent theauto adjustment feature of the camera from overriding the manualsetting. Next, adjust for hardware dependent parameters 3104 accesses alookup table and adjusts the demodulation algorithm based on hardwareand software differences. For the case of an image sensor, one exampleof this is changing the sampling time based on the rolling shutter speedof the device. This rolling shutter speed can either be loaded from alookup table beforehand (using predetermined values) or measured on thefly. Each device only needs to measure its rolling shutter speed onceper image sensor. Once parameters set? 3105 is satisfied the algorithmends; otherwise, it returns to identify regions of interest 3101.

The method of exploiting the metering area on a mobile device 103 can beused to optimize many of the required parameters in addition to theexposure, including white balance, contrast, saturation, ISO, gain,zoom, contrast, brightness, sharpness, resolution, image quality, andscene selection. Furthermore, these parameters could already be knownbeforehand, as each mobile device 103 will have its own “device profile”containing the optimal camera settings. This profile could be loadedclient side on the device, or sent over a server. Note that although themethod of using the metering area to control the exposure can improvethe performance of DPR demodulation, it is not strictly necessary.Simply locking the exposure 3103 is often sufficient to prevent theautomatic camera adjustments from filtering out the DPR signals.

Advanced Techniques for Decoding Information in DPR Modulated Signals

Once the sensors have been initialized 3001 and parameters have been set3104, FIG. 32 describes a process for decoding the information containedinside a DPR modulated signal. Identify regions 3201 is used to separatedifferent regions on the image illuminated by DPR signals. At the baselevel, the region of interest is the entire image. However, when one ormore light sources 101 are present, there exists an opportunity toreceive multiple DPR signals simultaneously. In this scenario, thesensor effectively acts as a multiple antenna receiver. Such multipleantenna systems, more generally referred to as multiple-inputmultiple-output (MIMO), are widely used in the wireless networkingspace. This is an example of spatial multiplexing, where wirelesschannels are allocated in space as opposed to time or frequency. Theimplications of MIMO for DPR demodulation in a beacon based lightpositioning system is that frequencies can be re-used in a space withoutworry of interference. When a mobile phone user receives DPR modulatedsignals on a photodiode array (such as an image sensor, or any imagingtechnology that contains multiple spatially separated sensors), the DPRsignals will each appear at different locations on the sensor. Eachregion 3201 of the image can then be processed independently, in thesame way that each mobile phone user in a cell network only connects tothe cell they are closest to.

This works in a way analogous to cellular phone networks. With cellularnetworks, mobile phone users only communicate with cellular towers thatare close to them. This allows multiple mobile phone users to share thesame frequency, provided they are all on different cells. In DPRmodulation, each light acts as its own cell transmitting uniquefrequencies. However, different lights can also use the same frequencyprovided that they are far enough apart. Re-using the same frequenciesin different space allows for greater system scalability, since lightingsources 101 can be installed at random without requiring the installerto worry about frequency allocation.

After sensors have been initialized 3001, and regions of interest 3201have been identified, detect frequency content 3202 identifies thepresence of DPR tones from the sensor data. We describe here multiplemethods for extracting the frequency content from a DPR signal. Onepossibility is to use line detection algorithms to identify the pixelwidth of the stripes, which directly corresponds to the transmittedfrequency. This stripe width is then used to access a lookup table thatassociates width and transmitted frequency and determines thetransmitted tones. Possible methods for detecting lines include Cannyedge detection, Hough Transforms, Sobel operators, differentials,Prewitt operators, and Roberts Cross detectors, all of which are welldeveloped algorithms, known to those of skill in the art. Adjust fordependent parameters 3004 then modifies the appropriate camera sensorsfor optimal DPR demodulation. In the case of line detection, thiscorresponds to a linear adjustment for the line width lookup table.Determine tones 3203 uses the adjusted line width to determine the DPRtone sent. This process is performed for each region on the image, untilthere are no more regions 3204 remaining A data structure containing allthe regions, with their associated identifiers, is then returned 3205.

An additional method for performing DPR demodulation is described inFIG. 33. One or more light sources 101 illuminates a scene 3301. Whenthe image sensor on mobile device 103 acquires a sequence of images3302, the brightness of any given pixel depends on both the details ofthe scene as well as the illumination. In this context, “scene” refersto the area within view of the camera. The scene dependence means thatpixels in the same row of the image will not all have the samebrightness, and the relative brightness of different image rows is notsolely dependent on the modulated illumination 3301. If one were to takethe Fourier transform of such an image, both the frequency content ofthe illumination, as well as the frequency content of the underlyingscene, will be present.

In order to recover the frequency content of the modulated illuminationindependently of the scene, the contribution of the scene may be removedusing a background subtraction algorithm 3303. The ‘background’ is theimage that would result from un-modulated illumination as opposed to theeffects of modulated illumination 3301. Subtracting the background froman image leaves only the effects of illumination modulation. Onepossible implementation of a background subtraction method uses a videosequence. If a video of a scene illuminated with modulated light isrecorded, the light and dark bands may appear at different locations ineach frame. For any modulation frequency that is not an exact multipleof the video frame rate, there will be a resulting beat frequencybetween the video frame frequency and the illumination modulationfrequency. The illumination signal will be in a different part of itsperiod at the beginning of each frame, and the light and dark bands willappear to be shifted between video frames (i.e. the bands will appear tomove up or down across the scene while the video is played). Althoughthis algorithm is described with the use of a video sequence, otherembodiments may perform background subtraction using still images.

Because the bands move between video frames, the average effect of thebands on any individual pixel value will be the same (assuming that in along enough video each pixel is equally likely to be in a light or darkband in any given frame). If all the video frames are averaged, theeffects of the bands (due to the illumination modulation) will bereduced to a constant value applied to each pixel location. If the videois of a motionless scene, this means that averaging the video frameswill remove the effect of the bands and reveal only the underlying scene(plus a constant value due to the averaged bands). This underlying scene(the background) may be subtracted from each frame of the video toremove the effects of the scene and leave only the effects ofillumination modulation 3301.

FIG. 34 contains an implementation of a possible background subtractionalgorithm 3304. A frame buffer 3402 accumulates video frames 3401. Thesize of this buffer can vary, depending on the memory capacity of mobiledevice 103 and the required time to first fix. Frame averaging 3403computes the average based on the frames in the buffer 3402. The averageof these frames is used to generate background frame 2704. Thebackground frame can be acquired using a number of different averagingtechniques 3403, including a simple numerical average, a normalizedaverage (where each frame is divided by the sum of all the frames),Gaussian averaging, or by doing a frame difference between subsequentframes. A frame difference simply subtracts subsequent frames from oneanother on a pixel-by-pixel basis.

For video of a scene with motion, simple averaging of video frames willnot yield the underlying scene background. FIG. 35 describes a techniquefor dealing with motion between frames, which is a likely scenario whendemodulating DPR signals on mobile device 103. Motion compensation 3501is necessary to best determine the underlying scene. By determining themotion between video frames (for example, shifting or rotation of thewhole scene due to camera movement), each video frame may be shifted ortransformed such that it overlies the previous frame as much aspossible. After performing these compensatory transforms on each framein motion compensation 3501, the video frames are averaged 3403 to getthe scene background 3404. Phase correlation is one possible method ofestimating global (i.e. the whole scene moves in the same way, as in thecase of camera motion while recording video) translational motionbetween frames. The 2D Fourier transform of a shifted image will be thesame as that of the original image, except that a phase shift will beintroduced at each point. Normalizing the magnitude of the 2D Fouriertransform and taking the inverse transform yields a 2D image with a peakoffset from the center of the image. The offset of this peak is the sameas the shift of the shifted image. Those skilled in the art willrecognize that additional methods for motion compensation 3501 includeKernel Density Estimators, Mean-shift based estimation, andEigenbackgrounds.

After removing the background scene, Fourier Analysis can be used torecover the DPR tone based on signals received from modulated lightsource 103. Specifics of this method are further described in FIG.36-43. FIG. 36 contains a sample image 3601 of a surface illuminated bya light source undergoing DPR modulation. The image is being recordedfrom a mobile device using a rolling shutter CMOS camera. The stripes3602 on the image are caused by the rolling shutter sampling function,which is modeled in by the sequence of Dirac Combs 2801 in FIG. 28.

FIG. 37 shows the result 3701 of performing background subtraction onthe raw image data from FIG. 36. Background subtraction is used toextract the stripes from the raw image data. The result is an image ofalternating black/white stripes that represents the discrete time-domainrepresentation of the transmitted DPR signal. The stripes 3702 are muchmore pronounced than in the raw image data from FIG. 36 due to theimprovement from background subtraction.

Illumination modulation affects each row of a video frame identically,but imperfect background subtraction may lead to non-identical pixelvalues across image rows. Taking the Fourier transform of row valuesalong different image columns, then, may produce different illuminationsignal frequency content results. Because the true illumination signalfrequency content is the same for the entire image, a technique toreconcile these different results may be employed. One possible methodis to assign the average pixel value for any given row to each pixel inthat row. This method takes into account the information from each pixelin the row, but by yielding uniform row values gives a singleillumination signal frequency content result when taking the Fouriertransform of row values along an image column. FIG. 38 displays theresults of applying row averaging 3801 to the background subtractedimage 3701. The stripes 3802 are much more visible as a result of therow averaging, and they are also more consistent across TOWS.

FIG. 39 shows the Fourier transform 3901 of the row averaged image 3801from FIG. 38. There is a peak frequency at the DPR tone of 700 Hz, aswell as a DC component at 0 Hz. The peak frequency is used to identifythe sequence of tones, and thus the transmitted identifier.

FIG. 40 shows the Fourier transform 4001 from FIG. 39 after applying ahigh-pass filter. The DC component of the signal is removed, whichallows a peak frequency detector to move to detection at the DPR tonefrequency.

FIG. 41 shows a 2-D Fast Fourier Transform 4101 of the post processedDPR modulated signal data 3701. In comparison to the 1-D Fourieranalysis performed in FIGS. 38-40, 2-D Fourier analysis of the DPRmodulated signal 3601 could also be performed. 2-D Fourier Analysis is apopular and widely used technique for image analysis. Because there area number of software libraries that are highly optimized for performingmultidimensional FFTs, including OpenCV, multidimensional Fourieranalysis is a viable alternative to the 1-D analysis. The DPR tones 4102can be easily seen across the vertical axis 4103 of the 2-D FFT.Brighter areas on the FFT image 4101 correspond to areas on the imagewith higher spectral content. A peak can be seen at the origin 4104,which corresponds to the DC component of the DPR signal.

FIG. 42 shows a low-pass filtered version 4201 of the 2-D FFT 4101. Thefiltered image 4201 contains dark areas 3502 at the higher frequencieson the image. The low pass filter rejects the higher frequencies. Thisis a key component of successful DPR demodulation. As discussedpreviously, DPR modulation relies on transmitting digital signals atdifferent frequencies. When using Fourier analysis on these signals,higher frequency harmonics appear, in particular at higher duty cycles.These higher frequency components act as noise in the signal, soremoving them with filtered image 4201 is one technique for recoveringthe transmitted tones.

When performing spectral analysis in the case of a 1-D FFT 3901 in FIG.39, it was necessary to remove the DC component of the DPR signal. PWMsignals 1901-1903 will contain a significant DC component, which needsto be filtered before moving on to extract the transmitted DPR tone.FIG. 43 shows a high-pass filtered version 4301 of the 2-D FFT 4101. Thedark area 4302 at DC demonstrates the result of the high-pass filter,which rejects the DC noise component. The higher frequency bands 4303are still contained in the signal, allowing the demodulator to determinethe peak frequency.

The techniques and methods disclosed for use in light based positioningsystems can be used with a variety of camera equipped mobile orstationary devices, such as: mobile phones, tablet computers, netbooks,laptops, desktops, or custom designed hardware. Further, the scope ofthe present invention is not limited to the above described embodiments,but rather is defined by the appended claims. These claims representmodifications and improvements to what has been described.

We claim:
 1. A method comprising: electronically receiving, by an imagesensor of a device, a digital video sequence of a scene, wherein: thedigital video sequence comprises a sequence of frames; the scenecomprises both modulated illumination broadcast by a beacon light sourceand un-modulated illumination; and each frame of the sequence of framescomprises a plurality of pixel values, each of the plurality of pixelvalues having a given point in the frame; calculating noise from thedigital video sequence, wherein the noise comprises information withinthe digital video sequence corresponding to the un-modulatedillumination of the scene; removing the calculated noise from thedigital video sequence to obtain an isolated digital video sequence ofthe modulated illumination of the scene; and demodulating the emittedlight signal from the isolated digital video sequence.
 2. The method ofclaim 1, wherein removing the calculated noise comprises subtracting theplurality of pixel values corresponding to the un-modulated illuminationof the scene from the plurality of pixel values corresponding to themodulated illumination of the scene.
 3. The method of claim 1,comprising accumulating the subset of the frames in a frame buffer. 4.The method of claim 1, comprising applying a motion compensationtechnique to transform each frame prior to calculating the noise.
 5. Themethod of claim 4, wherein the motion compensation technique comprisesone of shifting or transforming each frame of the sequence of frames tosubstantially overlie the previous frame.
 6. The method of claim 4,wherein the motion compensation technique comprises one of kerneldensity estimation, Eigenbackground, or mean-shift based estimation. 7.The method of claim 1, wherein calculating the noise comprises averagingthe plurality of pixel values of each given point of a subset of thesequence of frames.
 8. The method of claim 7, wherein the averaging isperformed using a technique comprising one of calculating a simplenumerical average, calculating a normalized average, calculating aGaussian average, or calculating the frame difference between subsequentframes.
 9. An image detection apparatus, comprising: an imaging sensor;and a processor in communication with the imaging sensor configured to:electronically receive a digital video sequence of a scene, wherein: thedigital video sequence comprises a sequence of frames; the scenecomprises both modulated illumination broadcast by a beacon light sourceand un-modulated illumination; and each frame of the sequence of framescomprises a plurality of pixel values, each of the plurality of pixelvalues having a given point in the frame; calculate noise from thedigital video sequence, wherein the noise comprises information withinthe digital video sequence corresponding to the un-modulatedillumination of the scene; remove the calculated noise from the digitalvideo sequence to obtain an isolated digital video sequence of themodulated illumination of the scene; and demodulate the emitted lightsignal from the isolated digital video sequence.
 10. The image detectionapparatus of claim 9, wherein the processor is configured to remove thecalculated noise using a technique comprising subtracting the pluralityof pixel values corresponding to the un-modulated illumination of thescene from the plurality of pixel values corresponding to the modulatedillumination of the scene.
 11. The image detection apparatus of claim 9,further comprising a frame buffer accessible by the processor, whereinthe processor is configured to accumulate the subset of the frames in aframe buffer.
 12. The image detection apparatus of claim 9, wherein theprocessor is configured to apply a motion compensation technique totransform each frame prior to calculating the noise.
 13. The imagedetection apparatus of claim 12, wherein the motion compensationtechnique comprises one of shifting or transforming each frame of thesequence of frames to substantially overlie the previous frame.
 14. Theimage detection apparatus of claim 12, wherein the motion compensationtechnique comprises one of kernel density estimation, Eigenbackground,or mean-shift based estimation.
 15. The image detection apparatus ofclaim 9, wherein the processor is configured to perform the calculatingof noise using a technique comprising averaging the plurality of pixelvalues of each given point of a subset of the sequence of frames. 16.The image detection apparatus of claim 15, wherein the processor isconfigured to perform averaging using a technique comprising one ofcalculating a simple numerical average, calculating a normalizedaverage, calculating a Gaussian average, or calculating the framedifference between subsequent frames.